System and method for intermachine markup language communications

ABSTRACT

A system, comprising a network interface, an additional data communications interface, and processor for supporting a control interface communicated through the network interface according to an intermachine markup language protocol, for controlling the network interface and the additional data communications interface.

The present application is a continuation of U.S. patent applicationSer. No. 10/693759 (copending) filed Oct. 24, 2003 now U.S. Pat. No.7,006,881, which is a continuation of U.S. patent application Ser. No.10/162,079 (copending) filed Jun. 3, 2002, now U.S. Pat. No. 6,640,145,which is a continuation of U.S. patent application Ser. No. 09/241,135filed Feb. 1, 1999, now U.S. Pat. No. 6,400,996.

A portion of the disclosure of this patent document and appendicescontain material which is subject to copyright protection. The copyrightowner has no objection to the facsimile reproduction by anyone of thispatent document or the patent disclosure, as it appears in the U.S.Patent and Trademark Office patent file or records, but otherwisereserves all copyright rights whatsoever.

FIELD OF THE INVENTION

The present invention relates to the field of adaptive systems, and moreparticularly systems and methods which are adaptive to a human userinput and/or a data environment, as well as applications for suchsystems and methods. More particularly, embodiments of the inventioninvolve, for example, consumer electronics, personal computers, controlsystems, and professional assistance systems.

BACKGROUND OF THE INVENTION

The prior art is rich in various systems and methods for data analysis,as well as various systems and methods relating to useful endeavors. Ingeneral, most existing systems and methods provide concrete functions,which have a defined response to a defined stimulus. Such systems, whileembodying the “wisdom” of the designer, have a particular shortcoming inthat their capabilities are static.

Intelligent or learning systems are also known. These systems arelimited by the particular paradigm employed, and rarely are the learningalgorithms general. In fact, while the generic theory and systems whichlearn are well known, the application of such systems to particularproblems requires both a detailed description of the problem, as well asknowledge of the input and output spaces. Even once these factors areknown, a substantial tuning effort may be necessary to enable acceptableoperation.

Therefore, the present invention builds upon the prior art, whichdefines various problems to be addressed, intelligent systems andmethods, tuning paradigms and user interfaces. Therefore, as set forthbelow, and in the attached appendix of references (including abstracts),incorporated herein by reference, a significant number of referencesdetail fundamental technologies which may be improved according to thepresent invention, or incorporated together to form a part of thepresent invention. To the some extent, these technologies are disclosedand are expressly incorporated herein by reference to avoid duplicationof prior art teachings. However, the disclosure herein is not meant tobe limiting as to the knowledge of a person of ordinary skill in theart. Recitation hereinbelow of these teachings or reference to theseteachings is not meant to imply that the inventors hereof werenecessarily in any way involved in these references, nor that theparticular improvements and claimed inventions recited herein were madeor conceived after the publication of these references. Thus, prior artcited herein is intended to (1) disclose information related to theapplication published before the filing hereof, (2) define the problemin the art to which the present invention is directed, (3) define priorart methods of solving various problems also addressed by the presentinvention; (4) define the state of the art with respect to methodsdisclosed or referenced herein; and/or (5) detail technologies used toimplement methods or apparatus in accordance with the present invention.

Human Interface

Aspects of the present invention provide an advanced user interface. Thesubject of man-machine interfaces has been studied for many years, andindeed the entire field of ergonomics and human factors engineeringrevolves around optimization of human-machine interfaces. Typically, theoptimization scheme optimizes the mechanical elements of a design, orseeks to provide a universally optimized interface. Thus, a single userinterface is typically provided for a system. In fact, some systemsprovide a variety of interfaces, for example, novice, intermediate andadvanced, to provide differing balances between available control andpresented complexity. Further, adaptive and/or responsive human-machinecomputer interfaces are now well known. However, a typical problempresented is defining a self-consistent and useful (i.e., an improvementover a well-designed static interface) theory for altering theinterface. Therefore, even where, in a given application, a theoryexists, the theory is typically not generalizable to other applications.Therefore, one aspect of the present invention is to provide such atheory by which adaptive and/or responsive user interfaces may beconstructed and deployed.

In a particular application, the user interface according to the presentinvention is applied to general-purpose-type computer systems, forexample, personal computers. One aspect of the present invention thusrelates to a programmable device that comprises a menu-driven interfacein which the user enters information using a direct manipulation inputdevice. Such a type of interface scheme is disclosed in Verplank,William L., “Graphics in Human-Computer Communication: Principles ofGraphical User-Interface Design”, Xerox Office Systems. See thereferences cited therein: Foley, J. D., Wallace, V. L., Chan, P., “TheHuman Factor of Computer Graphics Interaction Techniques”, IEEE CG&A,November 1984, pp. 13-48; Koch, H., “Ergonomische Betrachtung vonSchreibtastaturen”, Humane Production, 1, pp. 12-15 (1985); Norman, D.A., Fisher, D., “Why Alphabetic Keyboards Are Not Easy To Use: KeyboardLayout Doesn't Much Matter”, Human Factors 24(5), pp. 509-519 (1982);Perspectives: High Technology 2, 1985; Knowlton, K., “VirtualPushbuttons as a Means of Person-Machine Interaction”, Proc. of Conf.Computer Graphics, Pattern Recognition and Data Structure, BeverlyHills, Calif., May 1975, pp. 350-352; “Machine Now Reads, entersInformation 25 Times Faster Than Human Keyboard Operators”, InformationDisplay 9, p. 18 (1981); “Scanner Converts Materials to Electronic Filesfor PCs”, IEEE CG&A, December 1984, p. 76; “New Beetle Cursor DirectorEscapes All Surface Constraints”, Information Display 10, p. 12, 1984;Lu, C., “Computer Pointing Devices: Living With Mice”, High Technology,January 1984, pp. 61-65; “Finger Painting”, Information Display 12, p.18, 1981; Kraiss, K. F., “Neuere Methoden der Interaktion an derSchnittstelle Mensch-Maschine”, Z. F. Arbeitswissenschaft, 2, pp. 65-70,1978; Hirzinger, G., Landzettel, K., “Sensory Feedback Structures forRobots with Supervised Learning”, IEEE Conf. on Robotics and Automation,St. Louis, March 1985; Horgan, H., “Medical Electronics”, IEEE Spectrum,January 1984, pp. 90-93.

A menu based remote control-contained display device is disclosed inPlatte, Oberjatzas, and Voessing, “A New Intelligent Remote Control Unitfor Consumer Electronic Device”, IEEE Transactions on ConsumerElectronics, Vol. CE-31, No. 1, February 1985, 59-68.

A directional or direct manipulation-type sensor based infrared remotecontrol is disclosed in Zeisel, Tomas, Tomaszewski, “An InteractiveMenu-Driven Remote Control Unit for TV-Receivers and VC-Recorders”, IEEETransactions on Consumer Electronics, Vol. 34, No. 3, 814-818 (1988),which relates to a control for programming with the West GermanVideotext system. This implementation differs from the Videotextprogramming system than described in Bensch, U., “VPV—VIDEOTEXT PROGRAMSVIDEORECORDER”, IEEE Transactions on Consumer Electronics, Vol. 34, No.3, 788-792 (1988), which describes the system of Video Program SystemSignal Transmitters, in which the VCR is programmed by entering a codefor the Video Program System signal, which is emitted by televisionstations in West Germany. Each separate program has a unique identifiercode, transmitted at the beginning of the program, so that a user needonly enter the code for the program, and the VCR will monitor thechannel for the code transmission, and begin recording when the code isreceived, regardless of schedule changes. The Videotext ProgramsRecorder (VPV) disclosed does not intelligently interpret thetransmission, rather the system reads the transmitted code as a literallabel, without any analysis or determination of a classification of theprogram type.

Known manual input devices include the trackball, mouse, and joystick.In addition, other devices are known, including the so-called “J-cursor”or “mousekey” which embeds a two (x,y) or three (x,y,p) axis pressuresensor in a button conformed to a finger, present in a general purposekeyboard; a keyboard joystick of the type described in ElectronicEngineering Times, Oct. 28, 1991, p. 62, “IBM Points a New Way”; aso-called “isobar” which provides a two axis input by optical sensors(θ, x), a two and one half axis (x, y, digital input) input device, suchas a mouse or a “felix” device, infrared, acoustic, etc.; positionsensors for determining the position of a finger or pointer on a displayscreen (touch-screen input) or on a touch surface, e.g., “GlidePoint”(ALPS/Cirque); goniometer input (angle position, such as human jointposition detector), etc. Many of such suitable devices are summarized inKraiss, K. F., “Alternative Input Devices For Human ComputerInteraction”, Forschunginstitut Für Anthropotecahnik, Werthhoven, F. R.Germany. Another device, which may also be suitable is the GyroPoint,available from Gyration Inc., which provides 2-D or 3-D inputinformation in up to six axes of motion: height, length, depth, roll,pitch and yaw. Such a device may be useful to assist a user in inputtinga complex description of an object, by providing substantially moredegrees of freedom sensing than minimally required by a standard graphicuser interface. The many degrees of freedom available thus providesuitable input for various types of systems, such as “Virtual Reality”or which track a moving object, where many degrees of freedom and a highdegree of input accuracy is required. The Hallpot, a device which pivotsa magnet about a Hall effect sensor to produce angular orientationinformation, a pair of which may be used to provide information abouttwo axes of displacement, available from Elweco, Inc, Willoughby, Ohio,may also be employed as an input device.

User input devices may be broken down into a number of categories:direct inputs, i.e. touch-screen and light pen; indirect inputs, i.e.trackball, joystick, mouse, touch-tablet, bar code scanner (see, e.g.,Atkinson, Terry, “VCR Programming: Making Life Easier Using Bar Codes”),keyboard, and multi-function keys; and interactive input, i.e. Voiceactivation/instructions (see, e.g., Rosch, Winn L., “Voice Recognition:Understanding the Master's Voice”, PC Magazine, Oct. 27, 1987, 261-308);and eye tracker and data suit/data glove (see, e.g. Tello, Ernest R.,“Between Man And Machine”, Byte, September 1988, 288-293; products ofEXOS, Inc; Data Glove). Each of the aforementioned input devices hasadvantages and disadvantages, which are known in the art.

Studies suggest that a “direct manipulation” style of interface hasadvantages for menu selection tasks. This type of interface providesvisual objects on a display screen, which can be manipulated by“pointing” and “clicking” on them. For example, the popular GraphicalUser Interfaces (“GUIs”), such as Macintosh and Microsoft Windows, andothers known in the art, use a direct manipulation style interface. Adevice such as a touch-screen, with a more natural selection technique,is technically preferable to the direct manipulation method. However,the accuracy limitations and relatively high cost make other inputs morecommercially practical. Further, for extended interactive use,touchscreens are not a panacea for office productivity applications. Inaddition, the user must be within arms'0 length of the touch-screendisplay. In a cursor positioning task, Albert (1982) found the trackballto be the most accurate pointing device and the touch-screen to be theleast accurate when compared with other input devices such as the lightpen, joystick, data tablet, trackball, and keyboard. Epps (1986) foundboth the mouse and trackball to be somewhat faster than both thetouch-pad and joystick, but he concluded that there were no significantperformance differences between the mouse and trackball as compared withthe touch-pad and joystick.

It is noted that in text-based applications, an input device that isaccessible, without the necessity of moving the user's hands from thekeyboard, may be preferred. Thus, for example, Electronic EngineeringTimes (EET), Oct. 28, 1991, p. 62, discloses a miniature joystickincorporated into the functional area of the keyboard. This miniaturejoystick has been successfully incorporated into a number of laptopcomputers.

The following references are also relevant to the interface aspects ofthe present invention:

Hoffberg, Linda I, “AN IMPROVED HUMAN FACTORED INTERFACE FORPROGRAMMABLE DEVICES: A CASE STUDY OF THE VCR” Master's Thesis, TuftsUniversity (Master of Sciences in Engineering Design, November, 1990).

“Bar Code Programs VCR”, Design News, Feb. 1, 1988, 26.

“How to find the best value in VCRs”, Consumer Reports, March 1988,135-141.

“Low-Cost VCRs: More For Less”, Consumer Reports, March 1990, 168-172.

“Nielsen Views VCRs”, Television Digest, Jun. 23, 1988, 15.

“The Highs and Lows of Nielsen Homevideo Index”, Marketing & MediaDecisions, November 1985, 84-86+.

“The Quest for ‘User Friendly’”, U.S. News & World Report, Jun. 13,1988. 54-56.

“The Smart House: Human Factors in Home Automation”, Human Factors inPractice, December 1990, 1-36.

“VCR, Camcorder Trends”, Television Digest, Vol. 29:16 (Mar. 20, 1989).

“VCR's: A Look At The Top Of The Line”, Consumer Reports, March 1989,167-170.

“VHS Videocassette Recorders”, Consumer Guide, 1990, 17-20.

Abedini, Kamran, “An Ergonomically-improved Remote Control Unit Design”,Interface '87 Proceedings, 375-380.

Abedini, Kamran, and Hadad, George, “Guidelines For Designing BetterVCRs”, Report No. IME 462, Feb. 4, 1987.

Bensch, U., “VPV—VIDEOTEXT PROGRAMS VIDEORECORDER”, IEEE Transactions onConsumer Electronics, 34(3):788-792.

Berger, Ivan, “Secrets of the Universals”, Video, February 1989, 45-47+.

Beringer, D. B., “A Comparative Evaluation of Calculator Watch DataEntry Technologies: Keyboards to Chalkboards”, Applied Ergonomics,December 1985, 275-278.

Bier, E. A. et al. “MMM: A User Interface Architecture for SharedEditors on a Single Screen,” Proceedings of the ACM Symposium on UserInterface Software and Technology, Nov. 11-13, 1991, p. 79.

Bishop, Edward W., and Guinness, G. Victor Jr., “Human FactorsInteraction with Industrial Design”, Human Factors, 8(4):279-289 (August1966).

Brown, Edward, “Human Factors Concepts For Management”, Proceedings ofthe Human Factors Society, 1973, 372-375.

Bulkeley, Debra, “The Smartest House in America”, Design News, Oct. 19,1987, 56-61.

Card, Stuart K., “A Method for Calculating Performance times for Usersof Interactive Computing Systems”, IEEE, 1979, 653-658.

Carlson, Mark A., “Design Goals for an Effective User Interface”,Electro/82 Proceedings, 3/1/1-3/1/4.

Carlson, Mark A., “Design Goals for an Effective User Interface”, HumanInterfacing with Instruments, Session 3.

Carroll, Paul B., “High Tech Gear Draws Cries of “Uncle”, Wall StreetJournal, Apr. 27, 1988, 29.

Cobb, Nathan, “I don't get it”, Boston Sunday Globe Magazine, Mar. 25,1990, 23-29.

Davis, Fred, “The Great Look-and-Feel Debate”, A+, 5:9-11 (July 1987).

Dehning, Waltraud, Essig Heidrun, and Maass, Susanne, The Adaptation ofVirtual Man-Computer Interfaces to User Requirements in Dialogs,Germany: Springer-Verlag, 1981.

Ehrenreich, S. L., “Computer Abbreviations—Evidence and Synthesis”,Human Factors, 27(2):143-155 (April 1985).

Friedman, M. B., “An Eye Gaze Controlled Keyboard”, Proceedings of the2nd International Conference on Rehabilitation Engineering, 1984,446-447.

Gilfoil, D., and Mauro, C. L., “Integrating Human Factors and Design:Matching Human Factors Methods up to Product Development”, C. L. MauroAssoc., Inc., 1-7.

Gould, John D., Boies, Stephen J., Meluson, Antonia, Rasammy, Marwan,and Vosburgh, Ann Marie, “Entry and Selection Methods For SpecifyingDates”. Human Factors, 32(2):199-214 (April 1989).

Green, Lee, “Thermo Tech: Here's a common sense guide to the newthinking thermostats”, Popular Mechanics, October 1985, 155-159.

Grudin, Jonathan, “The Case Against User Interface Consistency”, MCCTechnical Report Number ACA-HI-002-89, January 1989.

Harvey, Michael G., and Rothe, James T., “VideoCassette Recorders: TheirImpact on Viewers and Advertisers”, Journal of Advertising, 25:19-29(December/January 1985).

Hawkins, William J., “Super Remotes”, Popular Science, February 1989,76-77.

Henke, Lucy L., and Donohue, Thomas R., “Functional Displacement ofTraditional TV Viewing by VCR Owners”, Journal of Advertising Research,29:18-24 (April-May 1989).

Hoban, Phoebe, “Stacking the Decks”, New York, Feb. 16, 1987, 20:14.

Howard, Bill, “Point and Shoot Devices”, PC Magazine, 6:95-97 (August1987).

Jane Pauley Special, NBC TV News Transcript, Jul. 17, 1990, 10:00 PM.

Kolson, Ann, “Computer wimps drown in a raging sea of technology”, TheHartford Courant, May 24, 1989, B1.

Kreifeldt, J. G., “A Methodology For Consumer Product Safety Analysis”,The 3rd National Symposium on Human Factors in Industrial Design inConsumer Products, August 1982, 175-184.

Kreifeldt, John, “Human Factors Approach to Medical Instrument Design”,Electro/82 Proceedings, 3/3/1-3/3/6.

Kuocheng, Andy Poing, and Ellingstad, Vernon S., “Touch Tablet and TouchInput”, Interface '87, 327.

Ledgard, Henry, Singer, Andrew, and Whiteside, John, Directions in HumanFactors for Interactive Systems, New York, Springer-Verlag, 1981.

Lee, Eric, and MacGregor, James, “Minimizing User Search Time MenuRetrieval Systems”, Human Factors, 27(2):157-162 (April 1986).

Leon, Carol Boyd, “Selling Through the VCR”, American Demographics,December 1987, 40-43.

Long, John, “The Effect of Display Format on the Direct Entry ofNumerical Information by Pointing”, Human Factors, 26(1):3-17 (February1984).

Mantei, Marilyn M., and Teorey, Toby J., “Cost/Benefit Analysis forIncorporating Human Factors in the Software Lifecycle”, Association forComputing Machinery, 1988.

Meads, Jon A., “Friendly or Frivolous”, Datamation, Apr. 1, 1988,98-100.

Moore, T. G. and Dartnall, “Human Factors of a Microelectronic Product:The Central Heating Timer/Programmer”, Applied Ergonomics, 1983,13(1):15-23.

Norman, Donald A., “Infuriating By Design”, Psychology Today,22(3):52-56 (March 1988).

Norman, Donald A., The Psychology of Everyday Things, New York, BasicBook, Inc. 1988.

Platte, Hans-Joachim, Oberjatzas, Gunter, and Voessing, Walter, “A NewIntelligent Remote Control Unit for Consumer Electronic Device”, IEEETransactions on Consumer Electronics, Vol. CE-31(1):59-68 (February1985).

Rogus, John G. and Armstrong, Richard, “Use of Human EngineeringStandards in Design”, Human Factors, 19(1):15-23 (February 1977).

Rosch, Winn L., “Voice Recognition: Understanding the Master's Voice”,PC Magazine, Oct. 27, 1987, 261-308.

Sarver, Carleton, “A Perfect Friendship”, High Fidelity, 39:42-49 (May1989).

Schmitt, Lee, “Let's Discuss Programmable Controllers”, Modern MachineShop, May 1987, 90-99.

Schniederman, Ben, Designing the User Interface: Strategies forEffective Human-Computer Interaction, Reading, Mass., Addison-Wesley,1987.

Smith, Sidney J., and Mosier, Jane N., Guidelines for Designing UserInterface Software, Bedford, Mass., MITRE, 1986.

Sperling, Barbara Bied, Tullis Thomas S., “Are You a Better ‘Mouser’ or‘Trackballer’? A Comparison of Cursor—Positioning Performance”, AnInteractive/Poster Session at the CHI+GI'87 Graphics Interface and HumanFactors in Computing Systems Conference.

Streeter, L. A., Ackroff, J. M., and Taylor, G. A. “On AbbreviatingCommand Names”, The Bell System Technical Journal, 62(6):1807-1826(July/August 1983).

Swanson, David, and Klopfenstein, Bruce, “How to Forecast VCRPenetration”, American Demographic, December 1987, 44-45.

Tello, Ernest R., “Between Man And Machine”, Byte, September 1988,288-293.

Thomas, John, C., and Schneider, Michael L., Human Factors in ComputerSystems, New Jersey, Ablex Publ. Co., 1984.

Trachtenberg, Jeffrey A., “How do we confuse thee? Let us count theways”, Forbes, Mar. 21, 1988, 159-160.

Tyldesley, D. A., “Employing Usability Engineering in the Development ofOffice Products”, The Computer Journal”, 31(5):431-436 (1988).

Verplank, William L., “Graphics in Human-Computer Communication:Principles of Graphical User-Interface Design”, Xerox Office Systems.

Voyt, Carlton F., “PLC's Learn New Languages”, Design News, Jan. 2,1989, 78.

Whitefield, A. “Human Factors Aspects of Pointing as an Input Techniquein Interactive Computer Systems”, Applied Ergonomics, June 1986, 97-104.

Wiedenbeck, Susan, Lambert, Robin, and Scholtz, Jean, “Using ProtocolAnalysis to Study the User Interface”, Bulletin of the American Societyfor Information Science, June/July 1989, 25-26.

Wilke, William, “Easy Operation of Instruments by Both Man and Machine”.Electro/82 Proceedings, 3/2/1-3/2/4.

Yoder, Stephen Kreider, “U.S. Inventors Thrive at Electronics Show”, TheWall Street Journal, Jan. 10, 1990, B1.

Zeisel, Gunter, Tomas, Philippe, Tomaszewski, Peter, “An InteractiveMenu-Driven Remote Control Unit for TV-Receivers and VC-Recorders”, IEEETransactions on Consumer Electronics, 34(3):814-818.

Agent Technologies

Presently well known human computer interfaces include so-called agenttechnology, in which the computer interface learns a task defined(inherently or explicitly) by the user and subsequently executes thetask. Such systems are available from Firefly (www.firefly.com), and arecommercially present in some on-line commerce systems, such asAmazon.com (www.amazon.com). See:

“ABI WHAP, Web Hypertext Applications Processor,”http://alphabase.com/abi3/whapinfo.html#profiling, (Jul. 11, 1996).

“AdForce Feature Set”, http://www.imgis.com/index.html/core/p2—2html(Apr. 11, 1997).

“IPRO,” http://www.ipro.com/, Internet profiles Corporation Home andother Web Pages (Jul. 11, 1996).

“Media Planning is Redefined in a New Era of Online Advertising,” PRNewswire, (Feb. 5, 1996).

“My Yahoo! news summary for My Yahoo! Quotes”, http://my.yahoo.com,(Jan. 27, 1997).

“NetGravity Announces Adserver 2.1”,http://www.netgravity.com/news/pressrel/launch21.html (Apr. 11, 1997).

“Netscape & NetGravity: Any Questions?”, http://www.netgravity.com/,(Jul. 11, 1996).

“Network Site Main”,http://www.doubleclick.net/frames/general/nets2set.htm (Apr. 11, 1997).

“Real Media,” http://www.realmedia.com/index.html, (Jul. 11, 1996).

“The Front Page”, http://live.excite.com/?aBb (Jan. 27, 1997) and (Apr.11, 1997).

“The Pointcast Network,” http:/www.pointcast.com/, (1996, Spring).

“The Power of PenPoint”, Can et al., 1991, p. 39, Chapter 13, pp.258-260.

“Welcome to Lycos,” http://www.lycos.com, (Jan. 27, 1997).

Abatemarco, Fred, “From the Editor”, Popular Science, September 1992, p.4

Berniker, M., “Nielsen plans Internet Service,” Broadcasting & Cable,125(30):34 (Jul. 24, 1995).

Berry, Deanne, et al. In an Apr. 10, 1990 news release, Symantecannounced a new version of MORE (TM).

Betts, M., “Sentry cuts access to naughty bits,” Computers and Security,vol. 14, No. 7, p. 615 (1995).

Boy, Guy A., Intelligent Assistant Systems, Harcourt Brace Jovanovich,1991, uses the term “Intelligent Assistant Systems”.

Bussey, H. E., et al., “Service Architecture, Prototype Description, andNetwork Implications of a Personalized Information Grazing Service,”IEEE Multiple Facets of Integration Conference Proceedings, vol. 3, No.Conf. 9, Jun. 3, 1990, pp. 1046-1053.

Donnelley, J. E., “WWW media distribution via Hopewise ReliabeMulticast,” Computer Networks and ISDN Systems, vol. 27, No. 6, pp.81-788 (April, 1995).

Edwards, John R., “Q&A: Integrated Software with Macros and anIntelligent Assistant”, Byte Magazine, January 1986, vol. 11, Issue 1,pp. 120-122, critiques the Intelligent Assistant by SymantecCorporation.

Elofson, G. and Konsynski, B., “Delegation Technologies: EnvironmentalScanning with Intelligent Agents”, Journal of Management InformationSystems, Summer 1991, vol. 8, Issue 1, pp. 37-62.

Garretson, R., “IBM Adds Drawing Assistant Design Tool to GraphicsSeries”, PC Week, Aug. 13, 1985, vol. 2, Issue 32, p. 8.

Gessler, S. and Kotulla A., “PDAs as mobile WWW browsers,” ComputerNetworks and ISDN Systems, vol. 28, No. 1-2, pp. 53-59 (December 1995).

Glinert-Stevens, Susan, “Microsoft Publisher: Desktop Wizardry”, PCSources, February, 1992, vol. 3, Issue 2, p. 357.

Goldberg, Cheryl, “IBM Drawing Assistant: Graphics for the EGA”, PCMagazine, Dec. 24, 1985, vol. 4, Issue 26, p. 255.

Hendrix, Gary G. and Walter, Brett A., “The Intelligent Assistant:Technical Considerations Involved in Designing Q&A's Natural-languageInterface”, Byte Magazine, December 1987, vol. 12, Issue 14, p. 251.

Hoffman, D. L. et al., “A New Marketing Paradigm for ElectronicCommerce,” (Feb. 19, 1996), http://www2000.ogsm.vanderbilt.edunovak/new.marketing.paradigm.html.

Information describing BroadVision One-to-One Application System:“Overview,” p. 1; Further Resources on One-To-One Marketing, p. 1;BroadVision Unleashes the Power of the Internet with PersonalizedMarketing and Selling, pp. 1-3; Frequently Asked Questions, pp. 1-3;Products, p. 1; BroadVision One-To-One(.TM.), pp. 1-2; Dynamic CommandCenter, p. 1; Architecture that Scales, pp. 1-2; Technology, pp. 1;Creating a New Medium for Marketing and Selling BroadVision One-To-Oneand the World Wide Web a White Paper, pp. 1-15;http://www.broadvision.com (January-March 1996).

Jones, R., “Digital's World-Wide Web server: A case study,” ComputerNetworks and ISDN Systems, vol. 27, No. 2, pp. 297-306 (November 1994).

McFadden, M., “The Web and the Cookie Monster,” Digital Age, (August1996).

Nadoli, Gajanana and Biegel, John, “Intelligent Agents in the Simulationof Manufacturing Systems”, Proceedings of the SCS Multiconference on AIand Simulation, 1989.

Nilsson, B. A., “Microsoft Publisher is an Honorable Start for DTPBeginners”, Computer Shopper, February 1992, vol. 12, Issue 2, p. 426,evaluates Microsoft Publisher and Page Wizard.

O'Connor, Rory J., “Apple Banking on Newton's Brain”, San Jose MercuryNews, Wednesday, Apr. 22, 1992.

Ohsawa, I. and Yonezawa, A., “A Computational Model of an IntelligentAgent Who Talks with a Person”, Research Reports on InformationSciences, Series C, April 1989, No. 92, pp. 1-18.

Pazzani, M. et al., “Learning from hotlists and coldlists: Towards a WWWInformation Filtering and Seeking Agent,” Proceedings InternationalConference on Tools with Artificial Intelligence, Janurary 1995, pp.492-495.

Poor, Alfred, “Microsoft Publisher”, PC Magazine, Nov. 26, 1991, vol.10, Issue 20, p. 40, evaluates Microsoft Publisher.

PRNewswire, information concerning the PointCast Network (PCN) (Feb. 13,1996) p. 213.

Raggett, D., “A review of the HTML+document format,” Computer Networksand ISDN Systems, vol. 27, No. 2, pp. 35-145 (November 1994).

Rampe, Dan, et al. In a Jan. 9, 1989 news release, Claris Corporationannounced two products, SmartForm Designer and SmartForm Assistant,which provide “Intelligent Assistance”, such as custom help messages,choice lists, and data-entry validation and formatting.

Ratcliffe, Mitch and Gore, Andrew, “Intelligent Agents take U.S. Bows.”,MacWeek, Mar. 2, 1992, vol. 6, No. 9, p. 1.

Sharif Heger, A. and Koen, B. V., “KNOWBOT: an Adaptive Data BaseInterface”, Nuclear Science and Engineering, February 1991, vol. 107,No. 2, pp. 142-157.

Soviero, Marcelle M., “Your World According to Newton”, Popular Science,September 1992, pp. 45-49.

Upendra Shardanand, “Social Information Filtering for MusicRecommendation” September 1994, pp. 1-93, Massachusetts Institute ofTechnology, Thesis.

Weber, Thomas E., “Software Lets Marketers Target Web Ads,” The WallStreet Journal, Apr. 21, 1997

Weiman, Liza and Moran, Tom, “A Step toward the Future”, Macworld,August 1992, pp. 129-131.

Yan, T. W. and Garcia-Molina, H., “SIFT—A Tool for Wide-Area InformationDissemination,” Paper presented at the USENIX Technical Conference, NewOrleans, La. (January 1995), pp. 177-186.

Industrial Controls

Industrial control systems are well known. Typically, a dedicatedreliable hardware module controls a task using a conventional algorithm,with a low level user interface. These devices are programmable, andtherfore a high level software program may be provided to translate userinstructions into the low level commands, and to analyze any returndata. See, U.S. Pat. No. 5,506,768, expressly incoporated herein byreference. See, also:

A. B. Corripio, “Tuning of Industrial Control Systems”, InstrumentSociety of America, Research Triangle Park, N.C. (1990) pp. 65-81.

C. J. Harris & S. A. Billings, “Self-Tuning and Adaptive Control: Theoryand Applications”, Peter Peregrinus LTD (1981) pp. 20-33.

C. Rohrer & Clay Nesler, “Self-Tuning Using a Pattern RecognitionApproach”, Johnson Controls, Inc., Research Brief 228 (Jun. 13, 1986).

D. E. Seborg, T. F. Edgar, & D. A. Mellichamp, “Process Dynamics andControl”, John Wiley & Sons, N.Y. (1989) pp. 294-307, 538-541.

E. H. Bristol & T. W. Kraus, “Life with Pattern Adaptation”, Proceedings1984 American Control Conference, pp. 888-892, San Diego, Calif. (1984).

Francis Schied, “Shaum's Outline Series-Theory & Problems of NumericalAnalysis”, McGraw-Hill Book Co., NY (1968) pp. 236, 237, 243, 244, 261.

K. J. Astrom and B. Wittenmark, “Adaptive Control”, Addison-WesleyPublishing Company (1989) pp. 105-215.

K. J. Astrom, T. Hagglund, “Automatic Tuning of PID Controllers”,Instrument Society of America, Research Triangle Park, N.C. (1988) pp.105-132.

R. W. Haines, “HVAC Systems Design Handbook”, TAB Professional andReference Books, Blue Ridge Summit, Pa. (1988) pp. 170-177.

S. M. Pandit & S. M. Wu, “Timer Series & System Analysis withApplications”, John Wiley & Sons, Inc., NY (1983) pp. 200-205.

T. W. Kraus 7 T. J. Myron, “Self-Tuning PID Controller Uses PatternRecognition Approach”, Control Engineering, pp. 106-111, June 1984.

Pattern Recognition

Another aspect of some embodiments of the invention relates to signalanalysis and complex pattern recognition. This aspect encompassesanalysis of any data set presented to the system: internal, userinterface, or the environment in which it operates. While semantic,optical and audio analysis systems are known, the invention is by nomeans limited to these types of data.

Pattern recognition involves examining a complex data set to determinesimilarities (in its broadest context) with other data sets, typicallydata sets which have been previously characterized. These data sets maycomprise multivariate inputs, sequences in time or other dimension, or acombination of both multivariate data sets with multiple dimensions.

The following cited patents and publications are relevant to patternrecognition and control aspects of the present invention, and are hereinexpressly incorporated by reference:

U.S. Pat. No. 5,067,163, incorporated herein by reference, discloses amethod for determining a desired image signal range from an image havinga single background, in particular a radiation image such as a medicalX-ray. This reference teaches basic image enhancement techniques.

U.S. Pat. No. 5,068,664, incorporated herein by reference, discloses amethod and device for recognizing a target among a plurality of knowntargets, by using a probability based recognition system. This patentdocument cites a number of other references, which are relevant to theproblem of image recognition:

Appriou, A., “Interet des theories de l'incertain en fusion de donnees”,Colloque International sur le Radar Paris, 24-28 avril 1989.

Appriou, A., “Procedure d'aide a la decision multi-informateurs.Applications a la classification multi-capteurs de cibles”, Symposium del'Avionics Panel (AGARD) Turquie, 25-29 avril 1988.

Arrow, K. J., “Social choice and individual valves”, John Wiley and SonsInc. (1963).

Bellman, R. E., L. A. Zadeh, “Decision making in a fuzzy environment”,Management Science, 17(4) (December 1970).

Bhatnagar, R. K., L. N. Kamal, “Handling uncertain information: a reviewof numeric and non-numeric methods”, Uncertainty in ArtificialIntelligence, L. N. Kamal and J. F. Lemmer, Eds. (1986).

Blair, D., R. Pollack, “La logique du choix collectif” Pour la Science(1983).

Chao, J. J., E. Drakopoulos, C. C. Lee, “An evidential reasoningapproach to distributed multiple hypothesis detection”, Proceedings ofthe 20th Conference on decision and control, Los Angeles, Calif.,December 1987.

Dempster, A. P., “A generalization of Bayesian inference”, Journal ofthe Royal Statistical Society, Vol. 30, Series B (1968).

Dempster, A. P., “Upper and lower probabilities induced by a multivaluedmapping”, Annals of mathematical Statistics, no. 38 (1967).

Dubois, D., “Modeles mathematiques de l'imprecis et de l'incertain envue d'applications aux techniques d'aide a la decision”, DoctoralThesis, University of Grenoble (1983).

Dubois, D., N. Prade, “Combination of uncertainty with belief functions:a reexamination”, Proceedings 9th International Joint Conference onArtificial Intelligence, Los Angeles (1985).

Dubois, D., N. Prade, “Fuzzy sets and systems-Theory and applications”,Academic Press, New York (1980).

Dubois, D., N. Prade, “Theorie des possibilites: application a larepresentation des connaissances en informatique”, Masson, Paris (1985).

Duda, R. O., P. E. Hart, M. J. Nilsson, “Subjective Bayesian methods forrule-based inference systems”, Technical Note 124-ArtificialIntelligence Center-SRI International.

Fua, P. V., “Using probability density functions in the framework ofevidential reasoning Uncertainty in knowledge based systems”, B.Bouchon, R. R. Yager, Eds. Springer Verlag (1987).

Ishizuka, M., “Inference methods based on extended Dempster and Shafer'stheory for problems with uncertainty/fuzziness”, New GenerationComputing, 1:159-168 (1983), Ohmsha, Ltd, and Springer Verlag.

Jeffrey, R. J., “The logic of decision”, The University of ChicagoPress, Ltd., London (1983)(2nd Ed.).

Kaufmann, A., “Introduction a la theorie des sous-ensembles flous”, Vol.1, 2 et 3-Masson-Paris (1975).

Keeney, R. L., B. Raiffa, “Decisions with multiple objectives:Preferences and value tradeoffs”, John Wiley and Sons, New York (1976).

Ksienski et al., “Low Frequency Approach to Target Identification”,Proc. of the IEEE, 63(12):1651-1660 (December 1975).

Kyburg, H. E., “Bayesian and non Bayesian evidential updating”,Artificial Intelligence 31:271-293 (1987).

Roy, B., “Classements et choix en presence de points de vue multiples”,R.I.R.O.-2eme annee-no. 8, pp. 57-75 (1968).

Roy, B., “Electre III: un algorithme de classements fonde sur unerepresentation floue des preferences en presence de criteres multiples”,Cahiers du CERO, 20(1):3-24 (1978).

Scharlic, A., “Decider sur plusieurs criteres. Panorama de l'aide a ladecision multicritere” Presses Polytechniques Romandes (1985).

Shafer, G., “A mathematical theory of evidence”, Princeton UniversityPress, Princeton, N.J. (1976).

Sugeno, M., “Theory of fuzzy integrals and its applications”, TokyoInstitute of Technology (1974).

Vannicola et al, “Applications of Knowledge based Systems toSurveillance”, Proceedings of the 1988 IEEE National Radar Conference,20-21 Apr. 1988, pp. 157-164.

Yager, R. R., “Entropy and specificity in a mathematical theory ofEvidence”, Int. J. General Systems, 9:249-260 (1983).

Zadeh, L. A., “Fuzzy sets as a basis for a theory of possibility”, Fuzzysets and Systems 1:3-28 (1978).

Zadeh, L. A., “Fuzzy sets”, Information and Control, 8:338-353 (1965).

Zadeh, L. A., “Probability measures of fuzzy events”, Journal ofMathematical Analysis and Applications, 23:421-427 (1968).

U.S. Pat. No. 5,067,161, incorporated herein by reference, relates to avideo image pattern recognition system, which recognizes objects in nearreal time.

U.S. Pat. Nos. 4,817,176 and 4,802,230, both incorporated herein byreference, relate to harmonic transform methods of pattern matching ofan undetermined pattern to known patterns, and are useful in the patternrecognition method of the present invention. U.S. Pat. No. 4,998,286,incorporated herein by reference, relates to a harmonic transform methodfor comparing multidimensional images, such as color images, and isuseful in the present pattern recognition methods.

U.S. Pat. No. 5,067,166, incorporated herein by reference, relates to apattern recognition system, in which a local optimum match betweensubsets of candidate reference label sequences and candidate templates.It is clear that this method is useful in the pattern recognitionaspects of the present invention. It is also clear that the interfaceand control system of the present invention are useful adjuncts to themethod disclosed in U.S. Pat. No. 5,067,166.

U.S. Pat. No. 5,048,095, incorporated herein by reference, relates tothe use of a genetic learning algorithm to adaptively segment images,which is an initial stage in image recognition. This patent has asoftware listing for this method. It is clear that this method is usefulin the pattern recognition aspects of the present invention. It is alsoclear that the interface and control system of the present invention areuseful adjuncts to the method disclosed in U.S. Pat. No. 5,048,095.

Fractal-Based Image Processing

Fractals are a relatively new field of science and technology thatrelate to the study of order and chaos. While the field of fractals isnow very dense, a number of relevant principles are applicable. First,when the coordinate axes of a space are not independent, and are relatedby a recursive algorithm, then the space is considered to have afractional dimensionality. One characteristic of such systems is that amapping of such spaces tends to have self-similarity on a number ofscales. Interestingly, natural systems have also been observed to haveself-similarity over several orders of magnitude, although as presentlybelieved, not over an unlimited range of scales. Therefore, one theoryholds that images of natural objects may be efficiently described byiterated function systems (IFS), which provide a series of parametersfor a generic formula or algorithm, which, when the process is reversed,is visually similar to the starting image. Since the “noise” of theexpanded data is masked by the “natural” appearance of the result,visually acceptable image compression may be provided at relatively highcompression ratios. This theory remains the subject of significantdebate, and, for example, wavelet algorithm advocates claim superiorresults for a more general set of starting images. It is noted that, ona mathematical level, wavelets and fractal theories have some commonthreads.

According to a particular embodiment of the invention, the expression ofan image as an ordered set of coefficients of an algorithm, wherein thecoefficients relate to elements of defined variation in scale, and theresulting set of coefficients is related to the underlying imagemorphology, is exploited in order to provide a means for patternanalysis and recognition without requiring decompression to anorthogonal coordinate space.

U.S. Pat. Nos. 5,065,447, and 4,941,193, both incorporated herein byreference, relate to the compression of image data by using fractaltransforms. These are discussed in detail below. U.S. Pat. No. 5,065,447cites a number of references, relevant to the use of fractals in imageprocessing:

U.S. Pat. No. 4,831,659.

“A New Class of Markov Processes for Image Encoding”, School ofMathematics, Georgia Inst. of Technology (1988), pp. 14-32.

“Construction of Fractal Objects with Iterated Function Systems”,Siggraph '85 Proceedings, 19(3):271-278 (1985).

“Data Compression: Pntng by Numbrs”, The Economist, May 21, 1988.

“Fractal Geometry-Understanding Chaos”, Georgia Tech Alumni Magazine, p.16 (Spring 1986).

“Fractal Modelling of Biological Structures”, Perspectives in BiologicalDynamics and Theoretical Medicine, Koslow, Mandell, Shlesinger, eds.,Annals of New York Academy of Sciences, vol. 504, 179-194 (dateunknown).

“Fractal Modelling of Real World Images, Lecture Notes for Fractals:Introduction, Basics and Perspectives”, Siggraph (1987).

“Fractals-A Geometry of Nature”, Georgia Institute of TechnologyResearch Horizons, p. 9 (Spring 1986).

A. Jacquin, “A Fractal Theory of Iterated Markov Operators withApplications to Digital Image Coding “, PhD Thesis, Georgia Tech, 1989.

A. Jacquin, “Image Coding Based on a Fractal Theory of IteratedContractive Image Transformations “p. 18, January 1992 (Vol 1 Issue 1)of IEEE Trans on Image Processing.

A. Jacquin, ‘Fractal image coding based on a theory of iteratedcontractive image transformations’, Proc. SPIE Visual Communications andImage Processing, 1990, pages 227-239.

A. E. Jacquin, ‘A novel fractal block-coding technique for digitalimages’, Proc. ICASSP 1990.

Baldwin, William, “Just the Bare Facts, Please”, Forbes Magazine, Dec.12, 1988.

Barnsley et al., “A Better Way to Compress Images”, Byte Magazine,January 1988, pp. 213-225.

Barnsley et al., “Chaotic Compression”, Computer Graphics World,November 1987.

Barnsley et al., “Harnessing Chaos For Images Synthesis”, ComputerGraphics, 22(4):131-140 (August, 1988).

Barnsley et al., “Hidden Variable Fractal Interpolation Functions”,School of Mathematics, Georgia Institute of Technology, Atlanta, Ga.30332, July, 1986.

Barnsley, M. F., “Fractals Everywhere”, Academic Press, Boston, Mass.,1988.

Barnsley, M. F., and Demko, S., “Iterated Function Systems and TheGlobal Construction of Fractals”, Proc. R. Soc. Lond., A399:243-275(1985).

Barnsley, M. F., Ervin, V., Hardin, D., Lancaster, J., “Solution of anInverse Problem for Fractals and Other Sets”, Proc. Natl. Acad. Sci.U.S.A., 83:1975-1977 (April 1986).

Beaumont J M, “Image data compression using fractal techniques”, BritishTelecom Technological Journal 9(4):93-108 (1991).

Byte Magazine, January 1988, supra, cites:

D. S. Mazel, Fractal Modeling of Time-Series Data, PhD Thesis, GeorgiaTech, 1991. (One dimensional, not pictures).

Derra, Skip, “Researchers Use Fractal Geometry, .”, Research andDevelopment Magazine, March 1988.

Elton, J., “An Ergodic Theorem for Iterated Maps”, Journal of ErgodicTheory and Dynamical Systems, 7 (1987).

Fisher Y, “Fractal image compression “, Siggraph 92.

Fractal Image Compression Michael F. Barnsley and Lyman P. Hurd ISBN0-86720-457-5, ca. 250 pp.

Fractal Image Compression: Theory and Application, Yuval Fisher (ed.),Springer Verlag, N.Y., 1995. ISBN number 0-387-94211-4.

Fractal Modelling of Biological Structures, School of Mathematics,Georgia Institute of Technology (date unknown).

G. E. Oien, S. Lepsoy & T. A. Ramstad, ‘An inner product space approachto image coding by contractive transformations’, Proc. ICASSP 1991, pp2773-2776.

Gleick, James, “Making a New Science”, pp. 215, 239, date unknown.

Graf S, “Barnsley's Scheme for the Fractal Encoding of Images”, JournalOf Complexity, V8, 72-78 (1992).

Jacobs, E. W., Y. Fisher and R. D. Boss. “Image Compression: A study ofthe Iterated Transform Method. Signal Processing 29, (1992) 25-263.

M. Barnsley, L. Anson, “Graphics Compression Technology, SunWorld,October 1991, pp. 42-52.

M. F. Barnsley, A. Jacquin, F. Malassenet, L. Reuter & A. D. Sloan,‘Harnessing chaos for image synthesis’, Computer Graphics, vol 22 no 4pp 131-140, 1988.

M. F. Barnsley, A. E. Jacquin, ‘Application of recurrent iteratedfunction systems to images’, Visual Comm. and Image Processing, volSPIE-1001, 1988.

Mandelbrot, B., “The Fractal Geometry of Nature”, W.H. Freeman & Co.,San Francisco, Calif., 1982, 1977.

Monro D M and Dudbridge F, “Fractal block coding of images”, ElectronicsLetters 28(11):1053-1054 (1992).

Monro D. M. & Dudbridge F. ‘Fractal approximation of image blocks’, ProcICASSP 92, pp. III: 485-488.

Monro D. M. ‘A hybrid fractal transform’, Proc ICASSP 93, pp. V: 169-72.

Monro D. M., Wilson D., Nicholls J. A. ‘High speed image coding with theBath Fractal Transform’, IEEE International Symposium on MultimediaTechnologies Southampton, April 1993.

Peterson, Ivars, “Packing It In-Fractals ..”, Science News,131(18):283-285 (May 2, 1987).

S. A. Hollatz, “Digital image compression with two-dimensional affinefractal interpolation functions”, Department of Mathematics andStatistics, University of Minnesota-Duluth, Technical Report 91-2. (anuts-and-bolts how-to-do-it paper on the technique).

Stark, J., “Iterated function systems as neural networks”, NeuralNetworks, Vol 4, pp 679-690, Pergamon Press, 1991.

Vrscay, Edward R. “Iterated Function Systems: Theory, Applications, andthe Inverse Problem. Fractal Geometry and Analysis, J. Belair and S.Dubuc (eds.) Kluwer Academic, 1991. 405-468.

U.S. Pat. No. 5,347,600, incorporated herein by reference, relates to amethod and apparatus for compression and decompression of digital imagedata, using fractal methods. According to this method, digital imagedata is automatically processed by dividing stored image data intodomain blocks and range blocks. The range blocks are subjected toprocesses such as a shrinking process to obtain mapped range blocks. Therange blocks or domain blocks may also be processed by processes such asaffine transforms. Then, for each domain block, the mapped range blockwhich is most similar to the domain block is determined, and the addressof that range block and the processes the blocks were subjected to arecombined as an identifier which is appended to a list of identifiers forother domain blocks. The list of identifiers for all domain blocks iscalled a fractal transform and constitutes a compressed representationof the input image. To decompress the fractal transform and recover theinput image, an arbitrary input image is formed into range blocks andthe range blocks processed in a manner specified by the identifiers toform a representation of the original input image.

“Image Compression Using Fractals and Wavelets”, Final Report for thePhase II Contract Sponsored by the Office of Naval Research, ContractNo. N00014-91-C-0117, Netrologic Inc., San Diego, Calif. (Jun. 2, 1993),relates to various methods of compressing image data, including fractalsand wavelets. This method may also be applicable in pattern recognitionapplications. This reference provides theory and comparative analysis ofcompression schemes.

A fractal-processing method based image extraction method is describedin Kim, D. H.; Caulfield, H. J.; Jannson, T.; Kostrzewski, A.; Savant,G, “Optical fractal image processor for noise-embedded targetsdetection”, Proceedings of the SPIE—The International Society forOptical Engineering, Vol. 2026, p. 144-9 (1993) (SPIE Conf: Photonicsfor Processors, Neural Networks, and Memories 12-15 July 1993, SanDiego, Calif., USA). According to this paper, a fractal dimensionalitymeasurement and analysis-based automatic target recognition (ATR) isdescribed. The ATR is a multi-step procedure, based on fractal imageprocessing, and can simultaneously perform preprocessing, interestlocating, segmenting, feature extracting, and classifying. See also,Cheong, C. K.; Aizawa, K.; Saito, T.; Hatori, M., “Adaptive edgedetection with fractal dimension”, Transactions of the Institute ofElectronics, Information and Communication Engineers D-II,J76D-II(11):2459-63 (1993); Hayes, H. I.; Solka, J. L.; Priebe, C. E.;“Parallel computation of fractal dimension”, Proceedings of the SPIE—TheInternational Society for Optical Engineering, 1962:219-30 (1993);Priebe, C. E.; Solka, J. L.; Rogers, G. W., “Discriminant analysis inaerial images using fractal based features”, Proceedings of the SPIE—TheInternational Society for Optical Engineering, 1962:196-208(1993). Seealso, Anson, L., “Fractal Image Compression”, Byte, October 1993, pp.195-202; “Fractal Compression Goes On-Line”, Byte, September 1993.

Methods employing other than fractal-based algorithms may also be used.See, e.g., Liu, Y., “Pattern recognition using Hilbert space”,Proceedings of the SPIE—The International Society for OpticalEngineering, 1825:63-77 (1992), which describes a learning approach, theHilbert learning. This approach is similar to Fractal learning, but theFractal part is replaced by Hilbert space. Like the Fractal learning,the first stage is to encode an image to a small vector in the internalspace of a learning system. The next stage is to quantize the internalparameter space. The internal space of a Hilbert learning system isdefined as follows: a pattern can be interpreted as a representation ofa vector in a Hilbert space. Any vectors in a Hilbert space can beexpanded. If a vector happens to be in a subspace of a Hilbert spacewhere the dimension L of the subspace is low (order of 10), the vectorcan be specified by its norm, an L-vector, and the Hermitian operatorwhich spans the Hilbert space, establishing a mapping from an imagespace to the internal space P. This mapping converts an input image to a4-tuple: t in P=(Norm, T, N, L-vector), where T is an operator parameterspace, N is a set of integers which specifies the boundary condition.The encoding is implemented by mapping an input pattern into a point inits internal space. The system uses local search algorithm, i.e., thesystem adjusts its internal data locally. The search is first conductedfor an operator in a parameter space of operators, then an errorfunction delta (t) is computed. The algorithm stops at a local minimumof delta (t). Finally, the input training set divides the internal spaceby a quantization procedure. See also, Liu, Y., “Extensions of fractaltheory”, Proceedings of the SPIE—The International Society for OpticalEngineering, 1966:255-68(1993).

Fractal methods may be used for pattern recognition. See, Sadjadi, F.,“Experiments in the use of fractal in computer pattern recognition”,Proceedings of the SPIE—The International Society for OpticalEngineering, 1960:214-22(1993). According to this reference, man-madeobjects in infrared and millimeter wave (MMW) radar imagery may berecognized using fractal-based methods. The technique is based onestimation of the fractal dimensions of sequential blocks of an image ofa scene and slicing of the histogram of the fractal dimensions computedby Fourier regression. The technique is shown to be effective for thedetection of tactical military vehicles in IR, and of airport attributesin MMW radar imagery.

In addition to spatial self-similarity, temporal self-similarity mayalso be analyzed using fractal methods. See, Reusens, E., “Sequencecoding based on the fractal theory of iterated transformations systems”,Proceedings of the SPIE—The International Society for OpticalEngineering, 2094(pt.1):132-40(1993). This reference describes a schemebased on the iterated functions systems theory which relies on a 3Dapproach in which the sequence is adaptively partitioned. Each partitionblock can be coded either by using the spatial self similarities or byexploiting temporal redundancies.

Fractal compression methods may be used for video data for transmission.See, Hurtgen, B.; Buttgen, P., “Fractal approach to low rate videocoding”, Proceedings of the SPIE—The International Society for OpticalEngineering, 2094(pt.1):120-31(1993). This reference relates to a methodfor fast encoding and decoding of image sequences on the basis offractal coding theory and the hybrid coding concept. The DPCM-loopaccounts for statistical dependencies of natural image sequences in thetemporal direction. Those regions of the original image where theprediction, i.e. motion estimation and compensation, fails are encodedusing an advanced fractal coding scheme, suitable for still images, andwhose introduction instead of the commonly used Discrete CosineTransform (DCT)-based coding is advantageous especially at very low bitrates (8-64 kbit/s). In order to increase reconstruction quality,encoding speed and compression ratio, some additional features such ashierarchical codebook search and multilevel block segmentation may beemployed. This hybrid technique may be used in conjunction with thepresent adaptive interface or other features of the present invention.

Fractal methods may be used to segment an image into objects havingvarious surface textures. See, Zhi-Yan Xie; Brady, M., “Fractaldimension image for texture segmentation”, ICARCV '92. SecondInternational Conference on Automation, Robotics and Computer Vision, p.CV-4.3/1-5 vol. 1, (1992). According to this reference, the fractaldimension and its change over boundaries of different homogeneoustextured regions is analyzed and used to segment textures in infraredaerial images. Based on the fractal dimension, different textures mapinto different fractal dimension image features, such that there issmooth variation within a single homogeneous texture but sharp variationat texture boundaries. Since the fractal dimension remains unchangedunder linear transformation, this method is robust for dismissingeffects caused by lighting and other extrinsic factors. Morphology isthe only tool used in the implementation of the whole process: texturefeature extraction, texture segmentation and boundary detection. Thismakes possible parallel implementations of each stage of the process.

Rahmati, M.; Hassebrook, L. G., “Intensity- and distortion-invariantpattern recognition with complex linear morphology”, PatternRecognition, 27 (4):549-68(1994) relates to a unified model basedpattern recognition approach is introduced which can be formulated intoa variety of techniques to be used for a variety of applications. Inthis approach, complex phasor addition and cancellation are incorporatedinto the design of filter(s) to perform implicit logical operationsusing linear correlation operators. These implicit logical operationsare suitable to implement high level gray scale morphologicaltransformations of input images. In this way non-linear decisionboundaries are effectively projected into the input signal space yet themathematical simplicity of linear filter designs is maintained. Thisapproach is applied to the automatic distortion- and intensity-invariantobject recognition problem. A set of shape operators or complex filtersis introduced which are logically structured into a filter bankarchitecture to accomplish the distortion and intensity-invariantsystem. This synthesized complex filter bank is optimally sensitive tofractal noise representing natural scenery. The sensitivity is optimizedfor a specific fractal parameter range using the Fisher discriminant.The output responses of the proposed system are shown for target,clutter, and pseudo-target inputs to represent its discrimination andgeneralization capability in the presence of distortion and intensityvariations. Its performance is demonstrated with realistic scenery aswell as synthesized inputs.

Sprinzak, J.; Werman, M., “Affine point matching”, Pattern RecognitionLetters, 15(4):337-9(1994) , relates to a pattern recognition method. Afundamental problem of pattern recognition, in general, is recognizingand locating objects within a given scene. The image of an object mayhave been distorted by different geometric transformations such astranslation, rotation, scaling, general affine transformation orperspective projection. The recognition task involves finding atransformation that superimposes the model on its instance in the image.This reference proposes an improved method of superimposing the model.

Temporal Image Analysis

Temporal image analysis is a well known field. This field holdssubstantial interest at present for two reasons. First, by temporalanalysis of a series of two dimensional images, objects and objectplanes may be defined, which provide basis for efficient yet generalalgorithms for video compression, such as the Motion Picture ExpertsGroup (MPEG) series of standards. Second, temporal analysis hasapplications in signal analysis for an understanding and analysis of thesignal itself.

U.S. Pat. No. 5,280,530, incorporated herein by reference, relates to amethod and apparatus for tracking a moving object in a scene, forexample the face of a person in videophone applications, comprisesforming an initial template of the face, extracting a mask outlining theface, dividing the template into a plurality (for example sixteen)sub-templates, searching the next frame to find a match with thetemplate, searching the next frame to find a match with each of thesub-templates, determining the displacements of each of thesub-templates with respect to the template, using the displacements todetermine affine transform coefficients and performing an affinetransform to produce an updated template and updated mask.

U.S. Pat. No. 5,214,504 relates to a moving video image estimationsystem, based on an original video image of time n and time n+1, thecentroid, the principal axis of inertia, the moment about the principalaxis of inertia and the moment about the axis perpendicular to theprincipal axis of inertia are obtained. By using this information, anaffine transformation for transforming the original video image at timen to the original video image at time n+1 is obtained. Based on theinfinitesimal transformation (A), {e^(At), and e^(A(t-1))} obtained bymaking the affine transformation continuous with regard to time isexecuted on the original video image at time n and time n+1. The resultsare synthesized to perform an interpolation between the frames.{e^((a(t-1))} is applied to the original video system time n+1. Thevideo image after time n+1 is thereby protected.

U.S. Pat. No. 5,063,603, incorporated herein by reference, relates to adynamic method for recognizing objects and image processing systemtherefor. This reference discloses a method of distinguishing betweendifferent members of a class of images, such as human beings. A timeseries of successive relatively high-resolution frames of image data,any frame of which may or may not include a graphical representation ofone or more predetermined specific members (e.g., particular knownpersons) of a given generic class (e.g. human beings), is examined inorder to recognize the identity of a specific member; if that member'simage is included in the time series. The frames of image data may beexamined in real time at various resolutions, starting with a relativelylow resolution, to detect whether some earlier-occurring frame includesany of a group of image features possessed by an image of a member ofthe given class. The image location of a detected image feature isstored and then used in a later-occurring, higher resolution frame todirect the examination only to the image region of the stored locationin order to (1) verify the detection of the aforesaid image feature, and(2) detect one or more other of the group of image features, if any ispresent in that image region of the frame being examined. By repeatingthis type of examination for later and later occurring frames, theaccumulated detected features can first reliably recognize the detectedimage region to be an image of a generic object of the given class, andlater can reliably recognize the detected image region to be an image ofa certain specific member of the given class. Thus, a human identityrecognition feature of the present invention may be implemented in thismanner. Further, it is clear that this recognition feature may form anintegral part of certain embodiments of the present invention. It isalso clear that the various features of the present invention would beapplicable as an adjunct to the various elements of the system disclosedin U.S. Pat. No. 5,063,603.

U.S. Pat. No. 5,067,160, incorporated herein by reference, relates to amotion-pattern recognition apparatus, having adaptive capabilities. Theapparatus recognizes a motion of an object that is moving and is hiddenin an image signal, and discriminates the object from the backgroundwithin the signal. The apparatus has an image-forming unit comprisingnon-linear oscillators, which forms an image of the motion of the objectin accordance with an adjacent-mutual-interference-rule, on the basis ofthe image signal. A memory unit, comprising non-linear oscillators,stores conceptualized meanings of several motions. A retrieval unitretrieves a conceptualized meaning close to the motion image of theobject. An altering unit alters the rule, on the basis of theconceptualized meaning. The image forming unit, memory unit, retrievalunit and altering unit form a holonic-loop. Successive alterations ofthe rules by the altering unit within the holonic loop change anambiguous image formed in the image forming unit into a distinct image.U.S. Pat. No. 5,067,160 cites the following references, which arerelevant to the task of discriminating a moving object in a background:

U.S. Pat. No. 4,710,964.

Shimizu et al, “Principle of Holonic Computer and Holovision”, Journalof the Institute of Electronics, Information and Communication,70(9):921-930 (1987).

Omata et al, “Holonic Model of Motion Perception”, IEICE TechnicalReports, Mar. 26, 1988, pp. 339-346.

Ohsuga et al, “Entrainment of Two Coupled van der Pol Oscillators by anExternal Oscillation”, Biological Cybernetics, 51:225-239 (1985).

U.S. Pat. No. 5,065,440, incorporated herein by reference, relates to apattern recognition apparatus, which compensates for, and is thusinsensitive to pattern shifting, thus being useful for decomposing animage or sequence of images, into various structural features andrecognizing the features. U.S. Pat. No. 5,065,440 cites the followingreferences, incorporated herein by reference, which are also relevant tothe present invention: U.S. Pat. Nos. 4,543,660, 4,630,308, 4,677,680,4,809,341, 4,864,629, 4,872,024 and 4,905,296.

Recent analyses of fractal image compression techniques have tended toimply that, other than in special circumstances, other image compressionmethods are “better” than a Barnsley-type image compression system, dueto the poor performance of compression processors and lower thanexpected compression ratios. Further, statements attributed to Barnsleyhave indicated that the Barnsley technique is not truly a “fractal”technique, but rather a vector quantization process which employs arecursive library. Nevertheless, these techniques and analyses havetheir advantages. As stated hereinbelow, the fact that the codesrepresenting the compressed image are hierarchical represents aparticular facet exploited by the present invention.

Another factor which makes fractal methods and analysis relevant to thepresent invention is the theoretical relation to optical imageprocessing and holography. Thus, while such optical systems maypresently be cumbersome and economically unfeasible, and theirimplementation in software models slow, these techniques neverthelesshold promise and present distinct advantages.

Biometric Analysis

Biometric analysis comprises the study of the differences betweenvarious organisms, typically of the same species. Thus, the intraspeciesvariations become the basis for differentiation and identification. Inpractice, there are many applications for biometric analysis systems,for example in security applications, these allow identification of aparticular human.

U.S. Pat. No. 5,055,658, incorporated herein by reference, relates to asecurity system employing digitized personal characteristics, such asvoice. The following references are cited:

“Voice Recognition and Speech Processing”, Elektor Electronics,September 1985, pp. 56-57.

Naik et al., “High Performance Speaker Verification .”, ICASSP 86,Tokyo, CH2243-4/86/0000-0881, IEEE 1986, pp. 881-884.

Shinan et al., “The Effects of Voice Disguise .”, ICASSP 86, Tokyo,CH2243-4/86/0000-0885, IEEE 1986, pp. 885-888.

Parts of this system relating to speaker recognition may be used toimplement a voice recognition system of the present invention fordetermining an actor or performer in a broadcast.

Neural Networks

Neural networks are a particular type of data analysis tool. There arecharacterized by the fact that the network is represented by a set of“weights”, which are typically scalar values, which are derived by aformula which is designed to reduce the error between a data patternrepresenting a known state and the network's prediction of that state.These networks, when provided with sufficient complexity and anappropriate training set, may be quite sensitive and precise. Further,the data pattern may be arbitrarily complex (although the computingpower required to evaluate the output will also grow) and thereforethese systems may be employed for video and other complex patternanalysis.

U.S. Pat. No. 5,067,164, incorporated herein by reference, relates to ahierarchical constrained automatic learning neural network for characterrecognition, and thus represents an example of a trainable neuralnetwork for pattern recognition, which discloses methods which areuseful for the present invention. This Patent cites various referencesof interest:

U.S. Pat. Nos. 4,760,604, 4,774,677 and 4,897,811.

LeCun, Y., Connectionism in Perspective, R. Pfeifer, Z. Schreter, F.Fogelman, L. Steels, (Eds.), 1989, “Generalization and Network DesignStrategies”, pp. 143-55.

LeCun, Y., et al., “Handwritten Digit Recognition: Applications ofNeural.”, IEEE Comm. Magazine, pp. 41-46 (November 1989).

Lippmann, R. P., “An Introduction to Computing with Neural Nets”, IEEEASSP Magazine, 4(2):4-22 (April 1987).

Rumelhart, D. E., et al., Parallel Distr. Proc.: Explorations inMicrostructure of Cognition, vol. 1, 1986, “Learning InternalRepresentations by Error Propagation”, pp. 318-362.

U.S. Pat. Nos. 5,048,100, 5,063,601 and 5,060,278, all incorporatedherein by reference, also relate to neural network adaptive patternrecognition methods and apparatuses. It is clear that the methods ofU.S. Pat. Nos. 5,048,100, 5,060,278 and 5,063,601 may be used to performthe adaptive pattern recognition functions of the present invention.More general neural networks are disclosed in U.S. Pat. Nos. 5,040,134and 5,058,184, both incorporated herein be reference, which providebackground on the use of neural networks. In particular, U.S. Pat. No.5,058,184 relates to the use of the apparatus in information processingand feature detection applications.

U.S. Pat. No. 5,058,180, incorporated herein by reference, relates toneural network apparatus and method for pattern recognition, and is thusrelevant to the intelligent pattern recognition functions of the presentinvention. This patent cites the following documents of interest:

U.S. Pat. Nos. 4,876,731 and 4,914,708.

Carpenter, G. A., S. Grossberg, “The Art of Adaptive Pattern Recognitionby a Self-Organizing Neural Network,” IEEE Computer, March 1988, pp.77-88.

Computer Visions, Graphics, and Image Processing 1987, 37:54-115.

Grossberg, S., G. Carpenter, “A Massively Parallel Architecture for aSelf-Organizing Neural Pattern Recognition Machine,” Computer Vision,Graphics, and Image Processing (1987, 37, 54-115), pp. 252-315.

Gullichsen E., E. Chang, “Pattern Classification by Neural Network: AnExperiment System for Icon Recognition,” ICNN Proceeding on NeuralNetworks, March 1987, pp. IV-725-32.

Jackel, L. D., H. P. Graf, J. S. Denker, D. Henderson and I. Guyon, “AnApplication of Neural Net Chips: Handwritten Digit Recognition,” ICNNProceeding, 1988, pp. II-107-15.

Lippman, R. P., “An Introduction to Computing with Neural Nets,” IEEEASSP Magazine, April 1987, pp. 4-22.

Pawlicki, T. F., D. S. Lee, J. J. Hull and S. N. Srihari, “NeuralNetwork Models and their Application to Handwritten Digit Recognition,”ICNN Proceeding, 1988, pp. II-63-70.

Chao, T.-H.; Hegblom, E.; Lau, B.; Stoner, W. W.; Miceli, W. J.,“Optoelectronically implemented neural network with a waveletpreprocessor”, Proceedings of the SPIE—The International Society forOptical Engineering, 2026:472-82(1993), relates to an optoelectronicneural network based upon the Neocognitron paradigm which has beenimplemented and successfully demonstrated for automatic targetrecognition for both focal plane array imageries and range-Doppler radarsignatures. A particular feature of this neural network architecturaldesign is the use of a shift-invariant multichannel Fourier opticalcorrelation as a building block for iterative multilayer processing. Abipolar neural weights holographic synthesis technique was utilized toimplement both the excitatory and inhibitory neural functions andincrease its discrimination capability. In order to further increase theoptoelectronic Neocognitron's self-organization processing ability, awavelet preprocessor was employed for feature extraction preprocessing(orientation, size, location, etc.). A multichannel optoelectronicwavelet processor using an e-beam complex-valued wavelet filter is alsodescribed.

Neural networks are important tools for extracting patterns from complexinput sets. These systems do not require human comprehension of thepattern in order to be useful, although human understanding of thenature of the problem is helpful in designing the neural network system,as is known in the art. Feedback to the neural network is integral tothe training process. Thus, a set of inputs is mapped to a desiredoutput range, with the network minimizing an “error” for the trainingdata set. Neural networks may differ based on the computation of the“error”, the optimization process, the method of altering the network tominimize the error, and the internal topology. Such factors are known inthe art.

Optical Pattern Recognition

Optical image processing holds a number of advantages. First, images aretypically optical by their nature, and therefore processing by thismeans may (but not always) avoid a data conversion. Second, many opticalimage processing schemes are inherently or easily performed in parallel,improving throughput. Third, optical circuits typically have responsetimes shorter than electronic circuits, allowing potentially short cycletimes. While many optical phenomena may be modeled using electroniccomputers, appropriate applications for optical computing, such aspattern recognition, hold promise for high speed in systems ofacceptable complexity.

U.S. Pat. No. 5,060,282, incorporated herein by reference, relates to anoptical pattern recognition architecture implementing the mean-squareerror correlation algorithm. This method allows an optical computingfunction to perform pattern recognition functions. U.S. Pat. No.5,060,282 cites the following references, which are relevant to opticalpattern recognition:

Kellman, P., “Time Integrating Optical Signal Processing”, Ph. D.Dissertation, Stanford University, 1979, pp. 51-55.

Molley, P., “Implementing the Difference-Squared Error Algorithm UsingAn Acousto-Optic Processor”, SPIE, 1098:232-239, (1989).

Molley, P., et al., “A High Dynamic Range Acousto-Optic Image Correlatorfor Real-Time Pattern Recognition”, SPIE, 938:55-65 (1988).

Psaltis, D., “Incoherent Electro-Optic Image Correlator”, OpticalEngineering, 23(1):12-15 (January/February 1984).

Psaltis, D., “Two-Dimensional Optical Processing Using One-DimensionalInput Devices”, Proceedings of the IEEE, 72(7):962-974 (July 1984).

Rhodes, W., “Acousto-Optic Signal Processing: Convolution andCorrelation”, Proc. of the IEEE, 69(1):65-79 (January 1981).

Vander Lugt, A., “Signal Detection By Complex Spatial Filtering”, IEEETransactions On Information Theory, IT-10, 2:139-145 (April 1964).

U.S. Pat. Nos. 5,159,474 and 5,063,602, expressly incorporated herein byreference, also relate to optical image correlators. Also of interest isLi, H. Y., Y. Qiao and D. Psaltis, Applied Optics (April, 1993). Seealso, Bains, S., “Trained Neural Network Recognizes Faces”, Laser FocusWorld, June, 1993, pp. 26-28; Bagley, H. & Sloan, J., “OpticalProcessing: Ready For Machine Vision?“, Photonics Spectra, August 1993,pp. 101-106.

Optical pattern recognition has been especially applied to twodimensional patterns. In an optical pattern recognition system, an imageis correlated with a set of known image patterns represented on ahologram, and the product is a pattern according to a correlationbetween the input pattern and the provided known patterns. Because thisis an optical technique, it is performed nearly instantaneously, and theoutput information can be reentered into an electronic digital computerthrough optical transducers known in the art. Such a system is describedin Casasent, D., Photonics Spectra, November 1991, pp. 134-140. Thereferences cited therein provide further details of the theory andpractice of such a system: Lendaris, G. G., and Stanely, G. L.,“Diffraction Pattern Sampling for Automatic Target Recognition”, Proc.IEEE 58:198-205 (1979); Ballard, D. H., and Brown, C. M., ComputerVision, Prentice Hall, Englewood Cliffs, N.J. (1982); OpticalEngineering 28:5 (May 1988)(Special Issue on product inspection);Richards J., and Casasent, D., “Real Time Hough Transform for IndustrialInspection” Proc. SPIE Technical Symposium, Boston 1989 1192:2-21(1989); Maragos, P., “Tutorial Advances in Morphological ImageProcessing” Optical Engineering 26:7:623-632 (1987); Casasent, D., andTescher, A., Eds., “Hybrid Image and Signal Processing II”, Proc. SPIETechnical Symposium, April 1990, Orlando Fla. 1297 (1990); Ravichandran,G. and Casasent, D., “Noise and Discrimination Performance of the MINACEOptical Correlation Filter”, Proc. SPIE Technical Symposium, April 1990,Orlando Fla., 1471 (1990); Weshsler, H. Ed., “Neural Nets For Human andMachine Perception”, Academic Press, New York (1991).

By employing volume holographic images, the same types of paradigms maybe applied to three dimensional images.

Query by Image Content

Query by image content, a phrase coined by IBM researchers, relates to asystem for retrieving image data stored in a database on the basis ofthe colors, textures, morphology or objects contained within the image.Therefore, the system characterizes the stored images to generate ametadata index, which can then be searched. Unindexed searching is alsopossible.

A number of query by image content systems are known, including bothstill and moving image systems, for example from IBM (QBIC), Apple(Photobook), Belmont Research Inc. (Steve Gallant), BrainTech Inc.;Center for Intelligent Information Retrieval (Umass Amherst), Virage,Inc., Informix Software, Inc. (Illustra), Islip Media, Inc., Magnifi,Numinous Technologies, Columbia University VisualSeek/WebSeek (Chang etal., John R. Smith), Monet (CWI and UvA), Visual Computing Laboratory,UC San Diego (ImageGREP, White and Jain). See also, ISO/IEC MPEG-7literature.

See, Jacobs, et al., “Fast Multiresolution Image Querying”, Departmentof Computer Science, University of Washington, Seattle Wash.

U.S. Pat. No. 5,655,117, expressly incorporated herein by reference,relates to a method and apparatus for indexing multimedia informationstreams for content-based retrieval. See also:

Gong et al, “An Image Database System with Content Capturing and FastImage Indexing Abilities”, PROC of the International Conference onMultimedia Computing and Systems, pp. 121-130 May 19, 1994.

Hongjiang, et al., Digital Libraries, “A Video Database System forDigital Libraries”, pp. 253-264, May 1994.

S. Abe and Y. Tonomura, Systems and Computers in Japan, vol. 24, No. 7,“Scene Retrieval Method Using Temporal Condition Changes”, pp. 92-101,1993.

Salomon et al, “Using Guides to Explore Multimedia Databases”, PROC ofthe Twenty-Second Annual Hawaii International Conference on SystemSciences. vol. IV, 3-6 Jan. 1989, pp. 3-12 vol. 4. Jan. 6, 1989.

Stevens, “Next Generation Network and Operating System Requirements forContinuous Time Media”, in Herrtwich (Ed.), Network and Operating SystemSupport for Digital Audio and Video, pp. 197-208, November 1991.

U.S. Pat. No. 5,606,655, expressly incorporated herein by reference,relates to a method for representing contents of a single video shotusing frames. The method provides a representative frame (Rframe) for agroup of frames in a video sequence, selecting a reference frame fromthe group of frames and storing the reference frame in a computermemory. This system defines a peripheral motion tracking region along anedge of the reference frame and successively tracks movement of boundarypixels in the tracking region, symbolizing any of the length of the shotand the presence of any caption. See, also:

“A Magnifier Tool for Video Data”, Mills et al., Proceedings of ACMComputer Human Interface (CHI), May 3-7, 1992, pp. 93-98.

“A New Family of Algorithms for Manipulating Compressed Images”, Smithet al., IEEE Computer Graphics and Applications, 1993.

“Anatomy of a Color Histogram”, Novak et al., Proceeding of ComputerVision and Pattern Recognition, Champaign, Ill., June 1992, pp. 599-605.

“Automatic Structure Visualization for Video Editing”, Ueda et al.,InterCHI'93 Conference Proceedings, Amsterdam, The Netherlands, 24-29Apr. 1993, pp. 137-141.

“Automatic Video Indexing and Full-Video Search for Object Appearances”,Nagasaka et al., Proceedings of the IFIP TC2/WG2.6 Second WorkingConference on Visual Database Systems, North Holland, Sep. 30-Oct. 3,1991, pp. 113-127.

“Color Indexing”, Swain et al., International Journal of ComputerVision, vol. 7, No. 1, 1991, pp. 11-32.

“Content Oriented Visual Interface Using Video Icons for Visual DatabaseSystems”, Tonomura et al., Journal of Visual Languages and Computing(1990) 1, pp. 183-198.

“Developing Power Tools for Video Indexing and Retrieval”, Zhang et al.,Proceedings of SPIE Conference on Storage and Retrieval for Image andVideo Databases, San Jose, Calif., 1994.

“Image Information Systems: Where Do We Go From Here?”, Chang et al.,IEEE Transactions on Knowledge and Data Engineering, vol. 4, No. 5,October 1992, pp. 431-442.

“Image Processing on Compressed Data for Large Video Databases”, Armanet al., Proceedings of First ACM International Conference on Multimedia,Anaheim, Calif., 1-6 Aug. 1993, pp. 267-272.

“Image Processing on Encoded Video Sequences”, Arman et al., ACMMultimedia Systems Journal, to appear 1994.

“Impact: An Interactive Natural-Motion-Picture Dedicated MultimediaAuthoring System”, Ueda et al., Proceedings of Human Factors inComputing Systems (CHI 91), New Orleans, La., Apr. 27-May 2, 1991, pp.343-350.

“MPEG: A Video Compression Standard for Multimedia Applications”, LeGall, Communications of the ACM, vol. 34, No. 4, April 1991, pp. 47-58.

“News On-Demand for Multimedia Networks”, Miller et al., ACMInternational Conference on Multimedia, Anaheim, Calif., 1-6, Aug. 1993,pp. 383-392.

“Overview of the px 64 kbit/s Video Coding Standard”, Liou,Communications of the ACM, vol. 34, No. 4, April 1991, pp. 60-63.

“Pattern Recognition by Moment Invariants”, Hu et al., Proc. IRE, vol.49, 1961, p. 1428.

“Pattern Recognition Experiments in the Mandala/Cosine Domain”, Hsu etal., IEEE Transactions on Pattern Analysis and Machine Intelligence,vol. PAMI-5, No. 5, September 1983, pp. 512-520.

“The JPEG Still Picture Compression Standard”, Wallace, Communicationsof the ACM, vol. 34, No. 4, April 1991, pp. 31-44.

“The Revised Fundamental Theorem of Moment Invariants”, Reiss, IEEETransactions on Pattern Analysis and Machine Intelligence, vol. 13, No.8, August 1991, pp. 830-834.

“VideoMAP and VideoSpacelcon: Tools for Anatomizing Video Content”,Tonomura et al., Inter CHI'93 Conference Proceedings, Amsterdam, TheNetherlands, 24-29 Apr., 1993, pp. 131-136.

“Visual Pattern Recognition by Moment Invariants”, IRE Trans. Inform.Theory, vol. 8, February 1962, pp. 179-187.

“Watch-Grab-Arrange-See: Thinking with Motion Images via Streams andCollages”, Elliott, Ph.D. Thesis, MIT, February 1993.

Book entitled Digital Image Processing, by Gonzalez et al.,Addison-Wesley, Readings, Mass., 1977.

Book entitled Digital Picture Processing by Rosenfeld et al., AcademicPress, Orlando, Fla., 1982.

Book entitled Discrete Cosine Transform—Algorithms, Advantages,Applications, by Rao et al., Academic Press, Inc., 1990.

Book entitled Sequential Methods in Pattern Recognition and MachineLearning, Fu, Academic, NY, N.Y. 1968.

C.-C. J. Kuo (ed), “Multimedia Storage and Archiving Systems”, SPIEProc. Vol. 2916 (11/18-11/22/96).

U.S. Pat. No. 5,600,775, expressly incorporated herein by reference,relates to a method and apparatus for annotating full motion video andother indexed data structures. U.S. Pat. No. 5,428,774, expresslyincorporated herein by reference relates to a system of updating anindex file of frame sequences so that it indexes non-overlapping motionimage frame sequences. U.S. Pat. No. 5,550,965, expressly incorporatedherein by reference, relates to a method and system for operating a dataprocessor to index primary data in real time with iconic table ofcontents. U.S. Pat. No. 5,083,860, expressly incorporated herein byreference, relates to a method for detecting change points in motionpicture images. U.S. Pat. No. 5,179,449, expressly incorporated hereinby reference, relates to a scene boundary detecting apparatus. See also:

“A show and tell of the QBIC technology—Query By Image Content (QBIC)”,IBM QBIC Almaden web site, pp. 1-4.

“Chaos & Non-Linear Models in Economics”.

“Chaos Theory in the Financial Markets. Applying Fractals, Fuzzy Logic,Genetic Algorithms”.

“Evolutionary Economics & Chaos Theory”.

“Four Eyes”, MIT Media Lab web site; pp. 1-2.

“Frequently asked questions about visual information retrieval”, VirageIncorporated web site; pp. 1-3.

“IBM Ultimedia Manager 1.1 and Clinet Search”, IBM software web site,pp. 1-4.

“Image Detection and Registration”, Digital Image Processing, Pratt,Wiley, N.Y., 1991.

“Jacob Methodology” @http://WWCSAI.diepa.unipa.it/researchprojects/jacob/jacob-method.html.

“Market Analysis. Applying Chaos Theory to Invstment & Economics”.

“Photobook”, MIT Media Lab web site; Aug. 7, 1996; pp. 1-2.

“Profiting from Chaos. Using Chaos Theory for Market Timing, StockSelection & Option”.

“Shape Analysis”, Digital Image Processing, Pratt, Wiley, N.Y., 1991.

“The QBIC Project”, IBM QBIC Almaden web site, home page (pp. 1-2).

“Virage—Visual Information Retrieval”, Virage Incorporated, home page.

“Virage Products”, Virage Incorporated web site; pp. 1-2.

“Visual Information Retrieval: A Virage Perspective Revision 3”, VirageIncorporated web site; 1995; pp. 1-13.

“Workshop Report: NSF—ARPA Workshop on Visual Information ManagementSystems”, Virage Incorporated web. site; pp. 1-15.

A. D. Bimbo, et al, “3-D Visual Query Language for Image Databases”,Journal Of Visual Languages & Computing, 1992, pp. 257-271.

A. E. Cawkell, “Current Activities in Image Processing Part III:Indexing Image Collections”, CRITique, vol. 4, No. 8, May 1992, pp.1-11, ALSIB, London.

A. Pizano et al, “Communicating with Pictorial Databases”, Human-MachineInteractive Systems, pp. 61-87, Computer Science Dept, UCLA, 1991.

A. Yamamoto et al, “Extraction of Object Features from Image and itsApplication to Image Retrieval”, IEEE 9th International Conference OnPattern Recognition, 1988, 988-991.

A. Yamamoto et al, “Image Retrieval System Based on Object Features”,IEEE Publication No. CH2518-9/87/0000-0132, 1987, pp. 132-134.

A. Yamamoto et al., “Extraction of Object Features and Its Applicationto Image Retrieval”, Trans. of IEICE, vol. E72, No. 6, 771-781 (June1989).

A. Yamamoto et al., “Extraction of Object Features from Image and ItsApplication to Image Retrieval”, Proc. 9th Annual Conference on PatternRecognition, vol. II, pp. 988-991 (November 1988).

A. Soffer and H. Samet. Retrieveal by content in symbolic-imagedatabases. In Symposium on Electronic Imaging: Science andTechnology—Storage & Retrieval for Image and Video Databases IV, pages144-155. IS&T/SPIE, 1996.

Abadi, M., et al, “Authentication and Delegation with Smart-cards”, Oct.22, 1990, revised Jul. 30, 1992 Report 67, Systems Research Center,Digital Equipment Corp., Palo Alto, Calif.

Advertisement for “TV Decision,“CableVision, Aug. 4, 1986.

American National Standard, “Financial Institution Retail MessageAuthentication”, ANSI X9.19 1986.

American National Standard, “Interchange Message Specification for Debitand Credit Card Message Exchange Among Financial Institutions”, ANSIX9.2-1988.

Antonofs, M., “Stay Tuned for Smart TV,” Popular Science, November 1990,pp. 62-65.

Arman et al., “Feature Management for Large Video Databases”, 1993.(Abstract Only).

ASIAN TECHNOLOGY INFORMATION PROGRAM (ATIP) REPORT: ATIP95.65: HumanComputer Interface International, 7/95 Yokohama.

Barber et al. “Ultimedia Manager: Query by Image Content and it'sApplications” IEE, 1994, pp. 424-429, January 1994.

Barros, et al. “Indexing Multispectral Images for Content-BasedRetrieval”, Proc. 23rd AIPR Workshop on Image and Information Retrieval,Proc. 23rd Workshop, Washington, D.C., October 1994, pp. 25-36.

Belkin, N. J., Croft, W. B., “Information Filtering And InformationRetrieval: Two Sides of the Same Coin?”, Communications of the ACM,December 1992, vol. 35, No. 12, pp. 29-38.

Benoit Mandelbrot: “Fractal Geometry of Nature”, W H Freeman and Co.,New York, 1983 (orig ed 1977).

Benoit Mandelbrot: “Fractals—Form, Chance and Dimensions”, W H Freemanand Co., San Francisco, 1977.

Bimbo et al., “Sequence Retrieval by Contents through Spatio TemporalIndexing”, IEEE on CD-ROM, pp. 88-92, Aug. 24, 1993.

Bolot, J.; Turletti, T. & Wakeman, I.; “Scalable Feedback Control forMulticast Video Distribution In the Internet”, Computer CommunicationReview, vol. 24, No. 4 October 1994, Proceedings of SIGCOMM 94, pp.58-67.

Bos et al., “SmartCash: a Practical Electronic Payment System”, pp. 1-8;August 1990.

Branko Pecar: “Business Forecasting for Management”, McGraw-Hill BookCo., London, 1994.

Brian H Kaye: “A Random Walk Through Fractal Dimensions”, VCHVerlagsgesellschaft, Weinheim, 1989.

Brugliera, Vito, “Digital On-Screen Display—A New Technology for theConsumer Interface”, Symposium Record Cable Sessions. Jun. 11, 1993, pp.571-586.

Burk et al, “Value Exchange Systems Enabling Security andUnobservability”, Computers & Security, 9 1990, pp. 715-721.

C. Chang et al, “Retrieval of Similar Pictures on Pictorial Databases”,Pattern Recognition, vol. 24, No. 7, 1991, pp. 675-680.

C. Chang, “Retrieving the Most Similar Symbolic Pictures from PictorialDatabases”, Information Processing & Management, vol. 28, No. 5, 1992.

C. Faloutsos et al, “Efficient and Effective Querying by Image Content”,Journal of Intelligent Information Systems:Integrating ArtificialIntelligence and Database Technologies, vol. 3-4, No. 3, July 1994, pp.231-262.

C. Goble et al, “The Manchester Multimedia Information System”,Proceedings of IEEE Conference, Eurographics Workshop, April, 1991, pp.244-268.

C. C. Chang and S. Y. Lee. Retrieval of similar pictures on pictorialdatabases. Pattern Recog., 24(7), 1991.

Case Study: The CIRRUS Banking Network, Comm. ACM 8, 28 pp. 7970-8078,August 1985.

Chalmers, M., Chitson, P., “Bead: Explorations In InformationVisualization”, 15th Ann. Int'l SIGIR 92/Denmark—June 1992, pp. 330-337.

Chang et al., “Intelligent Database Retrieval by Visual Reasoning”, PROCFourteenth Annual International Computer Software and ApplicationConference, 31 Oct.-1 Nov. 1990, pp. 459-464.

Chang, Yuh-Lin, Zeng, Wenjun, Kamel, Ibrahim, Alonso, Rafael,“Integrated Image and Speech Analysis for Content-Based Video Indexing”.

Chaum et al, “Untraceable Electronic Cash”, Advances in Cryptology,1988, pp. 319-327.

Chaum et al; “Achieving Electronic Privacy”, Scientific American, pp.319-327; 1988.

Chaum, D. “Security without Identification: Card Computers to Make BigBrother Obsolete”, Communications of the ACM, 28(10), October 1985, pp.1030-1044.

Chaum, D. “Untraceable Electronic Mail, Return Addresses, and DigitalPseudonyms”, Communications of the ACM, vol. 24, No. 2, February, 1981.

Chaum, D., “Achieving Electronic Privacy”, Scientific American, August1992, pp. 96-101.

Chaum, D. L. et al.; “Implementing Capability-Based Protection UsingEncryption”; Electronics Research Laboratory, College of Engineering,University of California, Berkeley, Calif.; Jul. 17, 1978.

Cliff Pickover, Spiral Symmetry (World Scientific).

Cliff Pickover, Chaos in Wonderland: Visual Adventures in a FractalWorld (St. Martin's Press).

Cliff Pickover, Computers and the Imagination (St. Martin's Press)

Cliff Pickover, Mazes for the Mind: Computers and the Unexpected (St.Martin's Press).

Cliff Pickover, Computers, Pattern, Chaos, and Beauty (St. Martin'sPress).

Cliff Pickover, Frontiers of Scientific Visualization (Wiley).

Cliff Pickover, Visions of the Future: Art, Technology, and Computing inthe 21st Century (St. Martin's Press).

Cohen, Danny; “Computerized Commerce”; ISI Reprint Series ISI/RS-89/243;October, 1989; Reprinted from Information Processing 89, Proceedings ofthe IFIP World Computer Congress, held Aug. 28-Sep. 1, 1989.

Cohen, Danny; “Electronic Commerce”; University of Southern California,Information Sciences Institute, Research Report ISI/RR-89-244; October,1989.

Common European Newsletter, Multimedia Content manipulation andManagement, http://ww.esat.kuleuven.ac.be/˜konijin/. . .

CompuServe Information Service Users Guide, CompuServe International,1986, pp. 109-114.

Computer Shopper, November 1994, “Internet for Profit”, pp. 180-182,187, 190-192, 522-528, 532, 534.

Computer, Vol. 28(9), September 1995.

Compuvid Sales Manual (date unknown).

Corporate Overview, Virage Incorporated web site; pp. 1-4.

Cox, Ingemar J., et al., “PicHunter: Bayesian Relevance Feedback forImage Retrieval,” Proc. of the ICPR '96, IEEE, pp. 361-369.

Cutting, D. R.; Karger, D. R.; Pedersen, J. O. & Tukey, J. W.“Scatter/Gather: A Cluster-based Approach to Browsing Large DocumentCollections”, 15 Ann. Int'l SIGIR '92, ACM, 1992, pp. 318-329.

D K Arrowsmith & C M Place: “An Introduction to Dynamical Systems”,Cambridge University Press, Cambridge, 1990.

Damashek, M., Gauging Similarity via N-Grams: Language-IndependentSorting, Categorization, and Retrieval of Text, pp. 1-11, Jan. 24, 1995.

Data Partner 1.0 Simplifies DB Query Routines, PC Week, Sep. 14, 1992,pp. 55 & 58.

David E Rumelhart & James L McClelland: “Parallel DistributedProcessing”, Vol 1., The MIT Press, Cambridge, Mass., 1986.

Deering, S.; Estrin, D.; Farinacci, D.; Jacobson, V.; Liu, C.; Wei, L;“An Architecture for Wide-Area Multicast Routing”, ComputerCommunication Review, vol. 24, No. 4, October 1994, Proceedings ofSIGCOMM 94, pp. 126-135.

Donal Daly: “Expert Systems Introduced”, Chartwell-Bratt, Lund, 1988.

Dukach, Seymon; Prototype Implementation of the SNPP Protocol;allspic.lcs.mit.edu; 1992.

E. Binaghi et al, “Indexing and Fuzzy Logic Based Retrieval of ColorImages”, Visual Database Systems, II, 1992, pp. 79-92.

E. Binaghi et al., “A Knowledge-Based Environment for Assessment ofColor Similarity”, Proc. 2nd Annual Conference on Topics for A1, pp.268-285 (1990).

E. Lee, “Similarity Retrieval Techniques”, Pictorial InformationSystems, Springer Verlag, 1980 pp. 128-176.

E. G. M. Petrakis and C. Faloutsos. Similarity searching in large imagedatabases. Technical Report 3388, Department of Computer Science,University of Maryland, 1995.

Edward Reitman: “Exploring the Geometry of Nature”, Windcrest Books,Blue Ridge Summit, 1989.

Even et al; “Electronic Wallet”; pp. 383-386;1983.

F. J. Varela and P. Bourgine (eds.): Proceedings of the first EuropeanConference on Artificial Life. Cambridge, Mass.: MIT Press. (1991).

Fassihi, Theresa & Bishop, Nancy, “Cable Guide Courting NationalAdvertisers,” Adweek, Aug. 8, 1988.

Flickner, et al. “Query by Image and Video Content, the QBIC System”,IEEE Computer 28(9); 23-32, 1995.

Foltz, P. W., Dumais, S. T., “Personalized Information Delivery: AnAnalysis Of Information Filtering Methods”, Communications of the ACM,December 1992, vol. 35, No. 12, pp. 51-60.

Frank Pettit: “Fourier Transforms in Action”, Chartwell-Bratt, Lund,1985.

G F Page, J B Gomm & D Williams: “Application of Neural Networks toModelling and Control”, Chapman & Hall, London, 1993.

G. Mannes, “Smart Screens”, Video Magazine, December 1993) (2 Pages).

G. Tortora et al, “Pyramidal Algorithms”, Computer Vision, Graphics andImages Processing, 1990, pp. 26-56.

Gautama, S., D'Haeyer, J. P. F., “Context Driven Matching in StructuralPattern Recognition”.

Gautama, S., Haeyer, J. D., “Learning Relational Models of Shape: AStudy of the Hypergraph Formalism”.

Gene F Franklin, J David Powell & Abbas Emami-Naeini: “Feedback Controlof Dynamic Systems”, Addison-Wesley Publishing Co. Reading, 1994.

George E P Box & Gwilym M Jenkins: “Time Series Analysis: Forecastingand Control”, Holden Day, San Francisco, 1976.

Gifford, D., “Notes on Community Information Systems”, MIT LCS TM-419,December 1989.

Gifford, David K.; “Cryptographic Sealing for Information Secrecy andAuthentication”; Stanford University and Xerox Palo Alto ResearchCenter; Communication of the ACM; vol. 25, No. 4; April, 1982.

Gifford, David K.; “Digital Active Advertising”; U.S. patent applicationSer. No. 08/168,519; filed Dec. 16, 1993.

Gligor, Virgil D. et al.; “Object Migration and Authentication”; IEEETransactions on Software Engineering; vol. SE-5, No. 6; November, 1979.

Gong et al. “An Image Database System with Content Capturing and FastImage Indexing Abilities” IEEE, 1994, pp. 121-130, May 1994.

Gregory L Baker & Jerry P Gollub: “Chaotic Dynamics: An Introduction”,Cambridge University Press, Cambridge, 1990.

Gupta, Amarnath; Weymount, Terry & Jain, Ramesh, “Semantic Queries WithPictures: The VIMSYS Model”, Proceedings of the 17th InternationalConference on Very Large Data Bases, pp. 69-79, Barcelona, September,1991.

H. Tamura et al, “Image Database Systems: A Survey”, PatternRecognition, vol. 17, No. 1, 1984, pp. 29-34.

H. Tamura, et al., “Textural Features Corresponding to VisualPerception, “IEEE Transactions on System, Man, and Cyb., vol. SMC-8, No.6, pp. 460-473 (1978).

H. Samet. The quadtree and related hierarchical data structures. ACMComputing Surveys, 16(2):187-260, 1984.

Hans Lauwerier: “Fractals—Images of Chaos”, Penguin Books, London, 1991.

Harty et al., “Case Study: The VISA Transaction Processing System,”1988.

Heinz-Otto Peitgen & Deitmar Saupe: “The Science of Fractal Images”,Springer-Verlag, N.Y., 1988.

Heinz-Otto Peitgen, Hartmut Jurgens & Deitmar Saupe: “Fractals for theClassroom”, Springer-Verlag, 1992.

Hirata, et al. “Query by Visual Example, Content Based Image Retrieval”Advance in Database Technology-EDBT '92, Springer-Verlag, Berlin 1992,pp. 56-71

Hirzalla et al., “A Multimedia Query User Interface”, IEEE on CD-ROM,pp. 590-593, Sep. 5, 1995.

Hooge, Charles, “Fuzzy logic Extends Pattern Recognition Beyond NeuralNetworks”, Vision Systems Design, January 1998, pp. 32-37.

Hou et al., “Medical Image Retrieval by Spatial Features”, IEEE onCD-ROM, pp. 1364-1369, Oct. 18, 1992.

Iino et al., “An Object-Oriented Model for Spatio-TemporalSynchronization of Multimedia Information”, May, 1994.

Information Network Institute, Carnegie Mellon University, InternetBilling Server, Prototype Scope Document, Oct. 14, 1993.

Ingemar J. Cox et al., “Target Testing and the Pic Hunter BayesianMultimedia Retrieval System,” Proc. of the 3 d Forum on Research andTechnology Advances in Digital Libraries, ADL '96, IEEE, pp. 66-75.

Intel Corporation, iPower Technology, Marketing Brochure, date unknown.

Intuit Corp. Quicken User's Guide, “Paying Bills Electronically”, pp.171-192; undated.

ISO/IEC JTC1/SC29/W11 N1735, MPEG97, July 1997- Stockholm, “MPEG-7Applications Document”.

ISO/IEC JTC1/SC29/WG11 N2460, MPEG98, October 1998 “MPEG-7 Context andObjectives (v.10—Atlantic City)”; ISO/IEC JTC1/SC29/WG11 N1920, MPEG97,October 1997 “MPEG-7 Context and Objectives (v.5—Fribourg)”; ISO/IECJTC1/SC29/WG11 N1733, MPEG97, July 1997, “MPEG-7 Context and Objectives(v.4—Stockholm)”.

ISO/IEC JTC1/SC29/WG11 N2461, MPEG98, October 1998—Atlantic City,“MPEG-7 Requirements”.

ISO/IEC JTC1/SC29/WG11 N2462, MPEG98, October 1998—Atlantic City,“MPEG-7 Applications”.

ISO/IEC JTC1/SC29/WG11 N2467, MPEG98, October 1998—Atlantic City,“MPEG-7 Content Set”.

Itzhak Wilf, “Computer, Retrieve For Me the Video Clip of the WinningGoal”, Advanced Imaging, August 1998, pp. 53-55.

Ivar Ekeland: “Mathematics and the Unexpected”, The University ofChicago Press, Chicago, 1988 Kenneth Falconer: “Fractal Geometry”, JohnWiley & Sons, Chichester, 1990.

Ivars Peterson: “The Mathematical Tourist”, W H Freeman, New York, 1988.

Iyengar et al., “Codes Designs for Image Browsing”, 1994.

J W Bruce & P J Giblin: “Curves and Singularities”, Cambridge UniversityPress, Cambridge, 1992.

J. Hasegawa et al, “Intelligent Retrieval of Chest X-Ray Image DatabaseUsing Sketches”, System And Computers In Japan, 1989, pp. 29-42.

J. M. Chassery, et al., “An Interactive Segmentation Method Based onContextual Color and Shape Criterion”, IEEE Transactions on PatternAnalysis and Machine Intelligence, vol. PAMI-6, No. 6, (November 1984).

J. Wachman, “A Video Browser that Learns by Example”, Masters Thesis,Massachusetts Institute of Technology; 1996; also appears as MIT MediaLaboratory Technical Report No. 383.

J. Hafner, H. S. Sawhney, W. Equitz, M. Flickner, and W. Niblack.Efficient color histogram indexing for quadratic form distancefunctions. IEEE Trans. Pattern Anal. Machine Intell., July 1995.

J. R. Bach, C. Fuller, A. Gupta, A. Hampapur, B. Horowitz, R. Humphrey,R. C. Jain, and C. Shu. Virage image search engine: an open frameworkfor image management. In Symposium on Electronic Imaging: Science andTechnology—Storage & Retrieval for Image and Video Databases IV, pages76-87. IS&T/SPIE, 1996.

J. R. Smith and S.-F. Chang. Querying by color regions using theVisualSEEk content-based visual query system. In M. T. Maybury, editor,Intelligent Multimedia Information Retrieval. IJCAI, 1996.

J. R. Smith and S.-F. Chang. Tools and techniques for color imageretrieval. In Symposium on Electronic Imaging: Science andTechnology—Storage & Retrieval for Image and Video Databases IV, volume2670, San Jose, Calif., February 1996. IS&T/SPIE.

Jacobs, Charles E., Finkelstein, Adam, Salesin, David H., “FastMultiresolution Image Querying”.

James Gleick: “Chaos—Making a New Science”, Heinemann, London, 1988.

Jane Hunter, “The Application of Metadata Standards to Video Indexing”http://www.dtsc.edu.au/RDU/staff/jane-hunter/EuroDL/final.html (<Dec.24, 1998).

Jim Binkley & Leslie Young, Rama: An Architecture for InternetInformation Filtering, Journal of Intelligent Information Systems:Integrating Artificial Intelligence and Database Technologies, vol. 5,No. 2, September 1995, pp. 81-99.

Jonathan Berry, “A Potent New Tool for Selling Database Marketing”,Business Week, Sep. 5, 1994, pp. 34-40.

Joseph L McCauley: “Chaos, Dymanics, and Fractals”, Cambridge UniversityPress, Cambridge, 1993.

JPL New Technology Report NPO-20213, Nasa Tech Brief Vol. 22, No. 4,Item #156 (April 1998).

Judith H. Irven et al., “Multi-Media Information Services: A LaboratoryStudy”, IEEE Communications Magazine, vol. 26, No. 6, June, 1988, pp.24-44.

K V Mardia, J T Kent & J M Bibby: “Multivariate Analysis”, AcademicPress, London, 1979.

K. Hirata et al, “Query by Visual Example Content Based ImageRetrieval”, Advances In Database Technology, March, 1992, pp. 57-71.

K. Wakimoto et al, “An Intelligent User Interface to an Image Databaseusing a Figure interpretation Method”, IEEE Publication No.CH2898-5/90/0000/0516, 1990, pp. 516-520.

K. Woolsey, “Multimedia Scouting”, IEEE Computer Graphics AndApplications, July 1991 pp. 26-38.

Kelly et al. “Efficiency Issues Related to Probability Density FunctionComparison”, SPIE vol. 2670, pp. 42-49 January 1996.

Kelly, P. M., et al. “Candid Comparison Algorithm for Navigating DigitalImage Databases”, Proceedings 7th International Working Conference onScientific and Statistical Database Management, pp. 252-258, 1994.

Krajewski, M. et al, “Applicability of Smart Cards to Network UserAuthentication”, Computing Systems, vol. 7, No. 1, 1994.

Krajewski, M., “Concept for a Smart Card Kerberos”, 15th NationalComputer Security Conference, October 1992.

Krajewski, M., “Smart Card Augmentation of Kerberos, Privacy andSecurity Research Group Workshop on Network and Distributed SystemSecurity”, February 1993.

Lampson, Butler; Abadi, Martin; Burrows, Michael; and Wobber, Edward;“Authentication in Distributed Systems: Theory and Practice”; ACMTransactions on Computer Systems; vol. 10, No. 4; November, 1992; pp.265-310.

Landis, Sean, “Content-Based Image Retrieval Systems for InteriorDesign”,http://www.tc.cornell.edu/Visualization/Education/cs718/fall1995/landis/index.html.

Langton C G (ed): Artificial Life; Proceedings of the firstinternational conference on Artificial life, Redwood City:Addison-Wessley (1989).

Lee et al., “Video Indexing—An Approach based on Moving Object andTrack”, Proceedings of Storage and Retrieval for Image and VideoDatabases, pp. 25-36. February 1993.

Lee, Denis, et al., “Query by Image Content Using Multiple Objects andMultiple Features: User Interface Issues,” 1994 Int'l Conf. on ImageProcessing, IEEE, pp. 76-80.

Lennart Ljung & Torsten Soderstrom: “Theory and Practice of RecursiveIdentification”, The MIT Press, Cambridge, Mass., 1983.

Lennart Ljung: “System Identification; Theory for the User”,Prentice-Hall Englewood Cliffs, N.J., 1987.

Loeb, S., “Architecting Personalized Delivery of MultimediaInformation”, Communications of the ACM, December 1992, vol. 35, No. 12,pp. 39-50.

M V Berry, I C Persival & N O Weiss: “Dynamical Chaos”, The RoyalSociety, London, 1987, Proceedings of a Royal Society Discussion Meetingheld on 4 & 5 Feb. 1987.

M. Bender, “EFTS: Electronic Funds Transfer Systems”, Kennikat Press,Port Washington, N.Y., pp. 43-46 1975.

M. H. O'Docherty et al, “Multimedia Information System—The Managementand Semantic Retrieval of all Electronic Data Types”, The ComputerJournal, vol. 34, No. 3, 1991.

M. loka, “A Method of Defining the Similarity of Images on the Basis ofColor Information”, Bulletin Of The National Museum Of Ethnology SpecialIssue, pp. 229-244, No. 17, November 1992.

M. Kurokawa, “An Approach to Retrieving Images by Using their PictorialFeatures”, IBM Research, Japan, September 1989.

M. Swain et al, “Color Indexing”, International Journal Of ComputerVision, 1991, pp. 12-32.

M. Stricker and A. Dimai. Color indexing with weak spatial constraints.In Symposium on Electronic Imaging: Science and Technology—Storage &Retrieval for Image and Video Databases IV, pages 29-41. IS&T/SPIE,1996.

M. Stricker and M. Orengo. Similarity of color images. In Storage andRetrieval for Image and Video Databases III, volume SPIE Vol. 2420,February 1995.

Mackay et al., “Virtual Video Editing in Interactive MultimediaApplications”, 1989.

Manners, George, “Smart Screens; Development of Personal NavigationSystems for TV Viewers,” Video Magazine, December 1993.

Martin Casdagli & Stephen Eubank: “Nonlinear Modelling and Forecasting”,Addison-Wesley Publishing Co., Redwood City, 1992.

Martinez et al. “Imagenet: A Global Distribution Database for ColorImage Storage and Retrieval in Medical Imaging Systems” IEEE, 1992,710-719, May 1992.

Marvin A. Sirbu; Internet Billing Service Design And PrototypeImplementation; pp. 1-19; An Internet Billing Server.

Masahiro Morita & Yoichi Shinoda, Information Filtering Based on UserBehavior Analysis and Best Match Text Retrieval, Proceedings of theSeventeenth Annual International ACM-SIGIR Conference on Research andDevelopment in Information Retrieval, Dublin, Jul. 3-6, 1994, PagesTitle Page (272)-281.

Medvinsy et al, “NetCash: A Design for Practical Electronic Currency onthe Internet”, Proc. 1st ACM Conf. on Comp. and Comm. Security, November1993.

Medvinsy et al., “Electronic Currency for the Internet”, ElectronicMarkets, pp. 30-31, September 1993.

Meyer, J. A., Roitblat, H. L., Wilson, W. (eds.): From Animals toAnimats. Proceedings of the Second International Conference onSimulation of Adaptive Behaviour. Cambridge, Mass.: MIT Press. (1991).

Middleton, G. V. ed., 1991, Nonlinear Dynamics, Chaos and Fractals, withApplications to Geological Systems. Geol. Assoc. Canada Short CourseNotes Vol. 9 (available from the GAC at Memorial University ofNewfoundland, St. John's NF AIB 3X5).

Mills, “Media Composition for Casual Users”, 1992.

Minneman et al., “Where Were We: making and using near-synchronous,pre-narrative video”, Multimedia '93, pp. 1-11. December 1993.

N. Hutheesing, “Interactivity for the passive”, Forbes magazine Dec. 6,1993 (@ Forbes Inc. 1993) (2 pages).

N. S. Chang et al., “Query-by-Pictorial Example”, IEEE Transactions onSoftware Engineering, vol. SE-6, No. 6, pp. 519-524 (November 1980).

N. S. Chang, et al., “Picture Query Languages for Pictorial Data-BaseSystems”, Computer vol. 14, No. 11, pp. 23-33 (November 1981).

Nagasaka et al., “Automatic Video Indexing and Full-Video Search forObject Appearances”, Visual Database Systems, (Knuth et al., eds.), pp.113-126. January 1992.

National Westminster Bank Group Brochure; pp. 1-29; undated.

Needham, Roger M. and Schroeder, Michael D.; “Using Encryption forAuthentication in Large Networks of Computers”; Communications of theACM; vol. 21, No. 12; December, 1978; pp. 993-999.

Needham, Roger M.; “Adding Capability Access to Conventional FileServers”; Xerox Palo Alto Research Center; Palo Alto, Calif.

Newman, B. C., “Proxy-Based Authorization and Accounting for DistributedSystems”, Proc. 13th Int. Conf. on Dist. Comp. Sys., May 1993.

Niblack, W. et al., “The QBIC Project: Querying Images by Content UsingColor, Texture, and Shape”, IBM Computer Science Research Report, pp.1-20 (Feb. 1, 1993).

Nussbaumer et al., “Multimedia Delivery on Demand: Capacity Analysis andImplications”, Proc 19th Conference on Local Computer Networks, 2-5 Oct.1994, pp. 380-386.

O. Guenther and A. Buchmann. Research issues in spatial databases. InACM SIGMOD Record, volume 19, December 1990.

Okamoto et al; “Universal Electronic Cash”, pp. 324-337; 1991.

Ono, Atsushi, et al., “A Flexible Content-Based Image Retrieval Systemwith Combined Scene Description Keyword,” Proc. of Multimedia '96, IEEE,pp. 201-208.

Otis Port, “Wonder Chips-How They'll Make Computing Power Ultrafast andUltracheap”, Business Week, Jul. 4, 1994, pp. 86-92.

P G Drazin: “Nonlinear System”, Cambridge University Press, Cambridge,1992.

P. Stanchev et al, “An Approach to Image Indexing of Documents”, VisualDatabase Systems, II, 1992, pp. 63-77.

Peter J Diggle: “Time Series: A Biostatistical Introduction”, ClarendonPress, Oxford, 1990.

Peters: “Chaos and Order in the Capital Markets”, Wiley, 1991Gershenfeld & Weigend: “The Future of Time Series”, Addison-Wesley,1993.

Pfitzmann et al; “How to Break and Repair a Provably Secure UntraceablePayment System”; pp. 338-350; 1991.

Phillips, “MediaView: a general multimedia digital publication system”,Comm. of the ACM, v. 34, n. 7, pp. 75-83. July 1991.

Predrag Cvitanovic: “Universality in Chaos”, Adam Hilger, Bristol, 1989.

R. Mehrotra et al, “Shape Matching Utilizing Indexed HypothesesGeneration and Testing”, IEEE Transactions On Robotics, vol. 5, No. 1,February 1989, pp. 70-77.

R. Price, et al., “Applying Relevance Feedback to a Photo ArchivalSystem”, Journal of Information Science 18, pp. 203-215 (1992).

R. W. Picard et al, “finding Similar Patterns in Large Image Databases”,IEEE ICASSP, Minneapolis, Minn., vol. V, pp. 161-164, April 1993; alsoappears in MIT Media Laboratory Technical Report No. 205.

Rangan et al., “A Window-based Editor for Digital Video and Audio”,January 1992.

Richards et al., “The Interactive Island”, IEE Revies, July/August 1991pp. 259-263.

Rivest, R.; “The MD5 Message-Digest Algorithm”; MIT Laboratory forComputer Science and RSA Data Security, Inc.; April, 1992.

Rivest, R. L. et al., “A Method for Obtaining Digital Signatures andPublic-Key Cryptosystems,” Laboratory for Computer Science,Massachusetts Institute of Technology, Cambridge, Mass.

Rivest, R. L.; Shamir, A. & Adleman, L.; “A Method for Obtaining DigitalSignatures and Public-Key Cryptosystems”, Communications of the ACM,February 1978, vol. 21, No. 2, pp. 120-126.

Robert Brown: “Statistical Forecasting for Inventory Control”,McGraw-Hill Book Co., New York, 1958.

Robinson, G., and Loveless, W., “Touch-Tone' Teletext—A CombinedTeletext-Viewdata System,” IEEE Transactions on Consumer Electronics,vol. CE-25, No. 3, July 1979, pp. 298-303.

Roizen, Joseph, “Teletext in the USA,” SMPTE Journal, July 1981, pp.602-610.

Rose, D. E.; Mander, R.; Oren, T., Ponceleon, D. B.; Salomon, G. & Wong,Y. Y. “Content Awareness in a File System Interface Implementing thePile Metaphor for Organizing Information”, 16 Ann. Int'l SIGIR '93, ACM,pp. 260-269.

Ross Anderson, “Why Cryptosystems Fail”, Proc. 1st Conf Computer andComm. Security, pp. 215-227, November 1993.

Ross J. Anderson, “UEPS—A Second Generation Electronic Wallet”, Proc. ofthe Second European Symposium on Research in Computer Security(ESORICS), Touluse, France, pp. 411-418, Touluse, France.

Rui, Yong, Huang, Thomas S., Chang, Shih-Fu, “Image Retrieval: PastPresent and Future”.

Rui, Yong, Huang, Thomas S., Mehotra, Sharad, “Browsing and retrievingVideo Content in a Unified Framework”.

Rui, Yong, Huang, Thomas S., Ortega, Michael, Mehotra, Sharad,“Relevance Feedback: A Power Tool for Interactive Content-Based ImageRetrieval”.

S. Chang et al, “An Intelligent Image Database System”, IEEETransactions On Software Engineering, vol. 14, No. 5, May 1988, pp.681-688.

S. Chang et al, “Iconic Indexing by 2-D Strings”, IEEE Transactions OnPattern Analysis And Machine Intelligence, vol. PAMI-9, No. 3, May 1987.

S. Chang et al, “Iconic Indexing by 2-D Strings”, IEEE Transactions OnPattern Analysis And Machine Intelligence, vol. 9, No. 3, May 1987, pp.413-427.

S. Charles et al, “Using Depictive Queries to Search PictorialDatabases”, Human Computer Interaction, 1990, pp. 493-498.

S. Lee et al, “2D C-string: A New Spatial Knowledge Representation forImage Database Systems”, Pattern Recognition, vol. 23, 1990, pp.1077-1087.

S. Lee et al, “Similarity Retrieval of Iconic Image Database”, PatternRecognition, vol. 22, No. 6 1989, pp. 675-682.

S. Lee et al, “Spatial Reasoning and Similarity Retrieval of ImagesUsing 2D C-string Knowledge Representation”, Pattern Recognition, 1992,pp. 305-318.

S. Negahdaripour et al “Challenges in Computer Vision: Future ResearchDirection”, IEEE Transactions On Systems, Man And Cybernetics, pp.189-199, 1992, at Conference on Computer Vision and Pattern Recognition.

S. Tanaka et al, “Retrieval Method for an Image Database based onTopological Structure”, SPIE, vol. 1153, 1989, pp. 318-327.

S.-F. Chang. Compressed-domain techniques for image/video indexing andmanipulation. In Proceedings, I.E.E.E. International Conference on ImageProcessing, Washington, D.C., October 1995. invited paper to the specialsession on Digital Library and Video on Demand.

S.-K. Chang, Q. Y. Shi, and C. Y. Yan. Iconic indexing by 2-D strings.IEEE Trans. Pattern Anal. Machine Intell., 9(3):413-428, May 1987.

S.-K. Chang. Principles of Pictorial Information Systems Design.Prentice Hall, 1989.

Salton, G., “Developments in Automatic Text Retrieval”, Science, vol.253, pp. 974-980, Aug. 30, 1991.

Schamuller-Bichl, I., “IC-Cards in High-Security Applications”, inSelected Papers from the Smart Card 2000 Conference, Springer Verlag,1991, pp. 177-199.

Semyon Dukach, “SNPP: A Simple Network Payment Protocol”, MIT Laboratoryfor Computer Science, Cambridge, Mass., 1993.

Shann et al. “Detection of Circular Arcs for Content-Based Retrievalfrom an Image Database” IEE Proc.-Vis. Image Signal Process, vol. 141,No. 1, February 1994, pp. 49-55.

Sheldon G Lloyd & Gerald D Anderson: “Industrial Process Control”,Fisher Controls Co., Marshalltown, 1971.

Sheth et al., “Evolving Agents for Personalized Information Filtering”,1-5 Mar. 1993, pp. 345-352.

Sheth, B. & Maes, P. “Evolving Agents For Personalized InformationFiltering”, Proc. 9th IEEE Conference, 1993 pp. 345-352.

Sincoskie, W. D. & Cotton C. J. “Extended Bridge Algorithms for LargeNetworks”, IEEE Network, January 1988 -vol. 2, No. 1, pp. 16-24.

Smith, J. et al., “Quad-Tree Segmentation for Texture-Based Image Query”Proceeding ACM Multimedia 94, pp. 1-15, San Francisco, 1994.

Smoliar, S. et al., “Content-Based Video Indexing and Retrieval”, IEEEMultimedia, pp. 62-72 (Summer 1994).

Society for Worldwide Interbank Financial Telecommunications S.C.,“A.S.W.I.F.T. Overview”, undated.

Spyros Makridakis & Steven Wheelwright: “The Handbook of Forecasting”,John Wiley, N.Y., 1982.

Steven C Chapra & Raymond P Canale: “Numerical Methods for Engineers”,McGraw-Hill Book Co., New York, 1988.

T. Arndt, “A Survey of Recent Research in Image Database Management”,IEEE Publication No. TH0330-1/90/0000/0092, pp. 92-97, 1990.

T. Gevers et al, “Enigma: An Image Retrieval System”, IEEE 11th IAPRInternational Conference On Pattern Recognition, 1992, pp. 697-700.

T. Gevers et al, “Indexing of Images by Pictorial Information”, VisualDatabase Systems, II, 1992 IFIP, pp. 93-101.

T. Kato et al, “A Cognitive Approach Interaction”, InternationalConference Of Multimedia Information Systems, January, 1991, pp.109-119.

T. Kato et al, “Trademark: Multimedia Database with AbstractedRepresentation on Knowledge Base”, Proceedings Of The SecondInternational Symposium On Interoperable Information Systems, pp.245-252, November 1988.

T. Kato et al, “Trademark: Multimedia Image Database System withIntelligent Human Interface”, System And Computers In Japan, 1990, pp.33-46.

T. Kato, “A Sketch Retrieval Method for Full Color Image Database-Queryby Visual Example”, IEEE, Publication No. 0-8186-2910-X/92, 1992, pp.530-533.

T. Kato, “Intelligent Visual Interaction with Image Database SystemsToward the Multimedia Personal Interface”, Journal Of InformationProcessing, vol. 14, No. 2, 1991, pp. 134-143.

T. Minka, “An Image Database Browser that Learns from User Interaction”,Masters Thesis, Massachusetts Institute of Technology; 1996; alsoappears as MIT Media Laboratory Technical Report 365.

T.-S. Chua, S.-K. Lim, and H.-K. Pung. Content-based retrieval ofsegmented images. In Proc. ACM Intern. Conf. Multimedia, October 1994.

Tak W. Yan & Hector Garcia-Molina, SIFT—A Tool for Wide-Area InformationDissemination, 1995 USENIX Technical Conference, New Orleans, La.,January 16-20, pp. 177-186.

Tanton, N. E., “UK Teletext—Evolution and Potential,” IEEE Transactionson Consumer Electronics, vol. CE-25, No. 3, July 1979, pp. 246-250.

Tenenbaum, Jay M. and Schiffman, Allan M.; “Development of NetworkInfrastructure and Services for Rapid Acquisition”; adapted from a whitepaper submitted to DARPA by MCC in collaboration with EIT and ISI.

Training Computers To Note Images, New York Times, Apr. 15, 1992.

Turcotte, Donald L., 1992, Fractals and Chaos in Geology and Geophysics.Cambridge U.P.

TV Communications Advertisement for MSI Datacasting Systems, January1973.

V. Gudivada et al, “A Spatial Similarity Measure for Image DatabaseApplications”, Technical Report 91-1, Department of Computer Science,Jackson, Miss., 39217, 1990-1991.

V. N. Gudivada and V. V. Raghavan. Design and evaluation of algorithmsfor image retrieval by spatial similarity. ACM Trans. on InformationSystems, 13(2), April 1995.

Vittal, J., “Active Message Processing: Message as Messengers”, pp.175-195; 1981.

Voydock, Victor et al.; “Security Mechanisms in High-Level NetworkProtocols”; Computing Surveys; vol. 15, No. 2; June 1981.

W Gellert, H Kustner, M Hellwich & H Kastner: “The VNR ConciseEncyclopedia of Mathematics”, Van Nostrand Reinhols Co., New York, 1975.

W. Grosky et al, “A Pictorial Index Mechanism for Model-based Matching”,Data 7 Knowledge Engineering 8, 1992, pp. 309-327.

W. Grosky et al, “Index-based Object Recognition in Pictorial DataManagement”, Computer Vision, 1990, pp. 416-436.

W. Niblack et al, “Find me the Pictures that Look Like This: IBM'S ImageQuery Project”, Advanced Imaging, April 1993, pp. 32-35.

W. Niblack, R. Barber, W. Equitz, M. Flickner, E. Glasman, D. Petkovic,P. Yanker, and C. Faloutsos. The QBIC project: Querying images bycontent using color, texture, and shape. In Storage and Retrieval forImage and Video Databases, volume SPIE Vol. 1908, February 1993.

W. T. Freeman et al, “The Design and Use of Steerable Filters”, IEEETransactions on Pattern Analysis and Machine Intelligence, vol. 15, No.9, September 1991, pp. 891-906.

Weber et al., “Marquee: A Tool for Real-Time Video Logging”, CHI '94.April 1994.

Willett, P., “Recent Trends in Hierarchic Document Clustering: ACritical Review”, Information Processing & Management, vol. 24, No. 5,pp. 557-597, 1988

William L. Thomas, “Electronic Program Guide Applications-The Basics ofSystem Design”, 1994 NCTA Technical Papers, pp. 15-20.

X. Zhang, et al, “Design of a Relational Image Database ManagementSystem: IMDAT”, IEEE Publication No. TH0166-9/87/0000-0310, 1987, pp.310-314.

Y. Okada, et al., “An Image Storage and Retrieval System for TextilePattern Adaptable to Color Sensation of the Individual”, Trans. Inst.Elec. Inf. Comm., vol. J70D, No. 12, pp. 2563-2574, December 1987(Japanese w/English Abstract).

Y. Yoshida et al, “Description of Weather Maps and Its Application toImplementation of Weather Map Database”, IEEE 7th InternationalConference On Pattern Recognition, 1984, pp. 730-733.

Yan et al., “Index Structures for Information Filtering Under the VectorSpace Model”, PROC the 10th International Conference on DataEngineering, pp. 14-18 of DRD203RW User's Manual relating to the DSSDigital System.

Z. Chen et al, “Computer Vision for Robust 3D Aircraft Recognition withFast Library Search”, Pattern Recognition, vol. 24, No. 5, pp. 375-390,1991, printed in Great Britain.

Zhuang, Yueting, Rui, Yong, Huang, Thomas S., Mehotra, Sharad, “ApplyingSemantic Association to Support Content-Based Video Retrieval”.

Video on Demand

Video on demand has long been sought as a means for deliveringpersonalized media content. The practical systems raise numerous issues,including data storage formats, retrieval software, server hardwarearchitecture, multitasking and buffering arrangements, physicalcommunications channel, logical communications channel, receiver anddecoder system, user interface, etc. In addition, typically apay-per-view concept may be employed, with concomitant subscription,royalty collection and accounting issues. See, e.g.:

A. D. Gelman, et al.: A Store-And-Forward Architecture ForVideo-On-Demand Service; ICC 91 Conf; June 1991; pp. 842-846.

Caitlin Bestler: Flexible Data Structures and Interface Rituals ForRapid Development of OSD Applications; 93 NCTA Tech. Papers; Jun. 6,1993; pp. 223-236.

Consumer Digest advertisement: Xpand Your TV's Capability: Fall/Winter1992; p. 215.

Daniel M. Moloney: Digital Compression in Todays Addressable Enviroment;1993 NCTA Technical Papers; Jun. 6, 1993; pp. 308-316.

Great Presentations advertisement: Remote, Remote; 1987; p. 32H.

Henrie van den Boom: An Interactive Videotex System for Two-Way CATVNetworks; AEU, Band 40; 1986; pp. 397-401.

Hong Kong Enterprise advertisement: Two Innovative New Consumer ProductsFrom SVI; November 1988; p. 379.

IEEE Communications Magazine; vol. 32, No. 5, May 1994 New York, N.Y.,US, pp. 68-80, XP 000451097 Chang et al “An Open Systems Approach toVideo on Demand”.

Proceedings of the IEEE, vol. 82, No. 4, April 1994 New York, N.Y., US,pp. 585-589, XP 000451419 Miller “A Scenario for the Deployment ofInteractive Multimedia Cable Television Systems in the United States inthe 1990's”.

Reimer, “Memories in my Pocket”, Byte, pp. 251-258, February 1991.

Sharpless, “Subscription teletext for value added services”, August1985.

Demographically Targeted Advertising Through Electronic Media

Since the advent of commercially subsidized print media, attempts havebeen made to optimize the placement and compensation aspects relating tocommercial messages or advertisements in media. In general, advertiserssubsidize a large percentage of the cost of mass publications andcommunications, in return for the inclusion and possibly strategicplacement of advertisements in the publication. Therefore, the cost ofadvertising in such media includes the cost of preparation of theadvertisement, a share of the cost of publication and a profit for thecontent provider and other services. Since the advertiser must bear someof the cost of production and distribution of the content, in additionto the cost of advertisement placement itself, the cost may besubstantial. The advertiser justifies this cost because the wide publicreception of the advertisement, typically low cost per consumer“impression”, with a related stimulation of sales due to commercialawareness of the advertisers' products and services. Therefore, theadvertisement is deemed particularly effective if either the audience isvery large, with ad response proportionate to the size of the audience,or if it targets a particularly receptive audience, with a response ratehigher than the general population.

On the other hand, the recipient of the commercial publication isgenerally receptive of the advertisement, even though it incurs apotential inefficiency in terms of increased data content andinefficiencies in receiving the content segment, for two reasons. First,the advertisements subsidize the publication, lowering the monetary costto the recipient. Second, it is considered economically efficient for arecipient to review commercial information relating to prospectivepurchases or expenditures, rather than directly soliciting suchinformation from the commercial source, i.e., “push” is better than“pull”. For this reason specialty publications are produced, includingcommercial messages appropriate for the particular content of the mediaor the intended recipients. In fact, in some forms of publications,most, if not all the information content is paid advertisements, withfew editorial or independently produced pieces.

Mass media, on the other hand, tends not to include specialty commercialmessages, because the interested population is too disperse and theresulting response rate from an advertisement too low, and furtherbecause the majority of the audience will be disinterested or evenrespond negatively to certain messages. Thus, mass media generallyincludes a majority of retail advertisements, with specialtyadvertisements relegated, if at all, to a classified section which isnot interspersed with other content.

This is the basis for a “least common denominator” theory of marketing,that mass media must merchandise to the masses, while specialty mediamerchandises to selected subpopulations. As a corollary, using suchtypes of media, it may be difficult to reach certain specializedpopulations who do not consistently receive a common set of publicationsor who receive primarily publications which are unspecialized ordirected to a different specialty.

Where a recipient has limited time for reviewing media, he or she mustdivide his or her available time between mass media and specialty media.Alternatively, publication on demand services have arisen which selectcontent based on a user's expressed interests. Presumably, these samecontent selection algorithms may be applied to commercial messages.However, these services are primarily limited distribution, and havecontent that is as variable as commercial messages. Likewise, mass mediaoften has regionally variable content, such as local commercials ontelevision or cable systems, or differing editions of print media fordifferent regions. Methods are known for demographic targeting ofcommercial information to consumers; however, both the delivery methodsand demographic targeting methods tend to be suboptimal.

Sometimes, however, the system breaks down, resulting in inefficiencies.These result where the audience or a substantial proportion thereof isinappropriate for the material presented, and thus realize a lowresponse rate for an advertiser or even a negative response for themedia due to the existence of particular commercial advertisers. Therecipients are bombarded with inappropriate information, while theadvertiser fails to realize optimal return on its advertisingexpenditures. In order to minimize the occurrence of these situations,services are available, including A. C. Nielsen Co. and Arbitron, Inc.,which seek to determine the demographics of the audience of broadcastmedia.

U.S. pat. No. 5,436,653, incorporated herein by reference, relates to abroadcast segment recognition system in which a signature representing amonitored broadcast segment is compared with broadcast segmentsignatures in a data base representing known broadcast segments todetermine whether a match exists. Therefore, the broadcast viewinghabits of a user may be efficiently and automatically monitored, withoutpre-encoding broadcasts or the like.

U.S. Pat. No. 5,459,306, incorporated herein by reference, relates to amethod for delivering targeting information to a prospective individualuser. Personal user information is gathered, as well as information on auser's use of a product, correlated and stored. Classes of informationpotentially relevant to future purchases are then identified, andpromotions and recommendations delivered based on the information andthe user information.

U.S. Pat. No. 5,483,278, incorporated herein by reference, relates to asystem having a user interface which can access downloaded electronicprograms and associated information records, and which can automaticallycorrelate the program information with the preferences of the user, tocreate and display a personalized information database based upon theresults of the correlation. Likewise, U.S. Pat. No. 5,223,914, expresslyincorporated herein by reference, relates to a system and method forautomatically correlating user preferences with a T.V. programinformation database.

U.S. Pat. No. 5,231,494, expressly incorporated herein by reference,relates to a system which selectively extracts one of a plurality ofcompressed television signals from a single channel based on viewercharacteristics.

U.S. Pat. No. 5,410,344 relates to a system for selecting video programsbased on viewers preferences, based on content codes of the programs.

U.S. Pat. No. 5,485,518, incorporated herein by reference, relates to asystem for electronic media program recognition and choice, allowing,for example, parental control of the individual programs presented,without requiring a transmitted editorial code.

Videoconferencing Technologies

Videoconferencing systems are well known in the art. A number ofinternational standards have been defined, providing varioustelecommunication bandwidth and communication link options. For example,H.320, H.323 and H.324 are known transport protocols over ISDN, packetswitched networks and public switched telephone networks, respectively.H.324 provides a multimedia information communication andvideoconferencing standard for communication over the standard “plainold telephone system” network (“POTS”), in which the video signal iscompressed using DCT transforms and motion compensation for transmissionover a v.80 synchronous v.34-type modem link. The video image isprovided as a video window with relatively slow frame rate. This image,in turn, may be presented on a computer monitor or television system,with appropriate signal conversion. See, Andrew W. Davis, “Hi Grandma!:Is It Time for TV Set POTS Videoconferencing?”, Advanced Imaging, pp.45-49 (March 1997); Jeff Child, “H.324 Paves Road For Mainstream VideoTelephony”, Computer Design, January 1997, pp. 107-110. A newly proposedset of extensions to H.324, called H.324/M, provides compatibility withmobile or impaired telecommunications systems, and accommodates errorsand distortions in transmissions, reduced or variable transmission ratesand other anomalies of known available mobile telecommunicationssystems, such as Cellular, GSM, and PCS.

Four common standards are employed, which are necessary forvideoconferencing stations to communicate with each other under commonstandards. The first is called h.320, and encompasses relatively highbandwidth systems, in increments of 64 kbits/sec digital communicationwith a synchronous communication protocol. Generally, these systemscommunicate with 128 kbits/sec, 256 kbits/sec or 384 kbits/sec, over anumber of “bonded” ISDN B-channels. The second standard H.324, employs astandard POTS communication link with a v.80/v.34bis modem,communicating at 33.6 kbits/sec synchronous. The third standard, is thenewly established H.321 standard, which provides for videoconferencingover a packet switched network, such as Ethernet, using IPX or TCP/IP.Finally, there are so-called Internet videophone systems, such as IntelProshare. See, Andrew W. Davis, “The Video Answering Machine: IntelProShare's Next Step”, Advanced Imaging, pp. 28-30 (March 1997).

In known standards-based videoconferencing systems, the image isgenerally compressed using a discrete cosine transform, which operatesin the spatial frequency domain. In this domain, visually unimportantinformation, such as low frequencies and high frequency noise areeliminated, leaving visually important information. Further, becausemuch of the information in a videoconference image is repeated insequential frames, with possible movement, this redundant information istransmitted infrequently and filtered from the transmitted image stream,and described with motion vector information. This motion vectorinformation encodes objects which are fixed or move somewhat betweenframes. Such known techniques include H.261, with integer pixel motionestimation, and H.263, which provides ½ pixel motion estimation. Othertechniques for video compression are known or have been proposed, suchas H.263+, and MPEG-4 encoding. Many standard videoconferencingprotocols require the initial transmission of a full frame image, inorder to set both transmitting and receiving stations to the sameencoding state. The digital data describing this image is typicallyHuffman encoded for transmission. Multiple frames may be combined andcoded as a unit, for example as so-called PB frames. Other techniquesare also known for reducing image data transmission bandwidth forvarious applications, including video conferencing.

Each remote videoconference terminal has an interface system, whichreceives the digital data, and separates the video information (H.261,H.263), audio information (G.711, G.723, G.723.1), data protocolinformation (HDLC, V.14, LAPM, etc.) and control information (H.245,H.221/H.223) into discrete streams, which are processed separately.Likewise, each terminal interface system also assembles the audioinformation, video information, data protocols and control data fortransmission. The control information consists of various types ofinformation; the standard control protocol which addresses the dataformat, error correction, exception handling, and other types ofcontrol; and the multipoint control information, such as which remotevideoconference terminal(s) to receive audio information from, selectiveaudio muting, and such. Generally, the standard, low level controlinformation is processed locally, at the codec interface system, andfiltered from the remainder of the multipoint control system, with onlythe extracted content information made available to the other stations.

The ITU has developed a set of multipoint videoconferencing standards orrecommendations, T.120-T.133, T.RES series, H.231, H.243, etc. Thesedefine control schemes for multiple party video conferences. Typically,these protocols are implemented in systems which either identicallyreplicate the source image data stream from one source to a plurality ofdestinations, or completely decode and reencode the image in a differentformat in a “transcoder” arrangement, to accommodate incompatibleconference stations. The ITU standards also allow optional data fieldswhich may be used to communicate digital information essentially outsidethe videoconference scheme, and provide data conferencing capabilities,which allow videoconferencing and data conferencing to proceedsimultaneously. See, ITU T.120-T.127, T.130-T.133, T.RES, T.Share andT.TUD recommendations, expressly incorporated herein by reference.

There are a number of known techniques for transmitting and displayingalphanumeric data on a television, the most common of which areteletext, used primarily in Europe, and closed caption, which ismandated in television sets larger than 13 inches by the TelevisionDecoder Circuitry Act of 1990, and Section 305 of the TelecommunicationsAct of 1996, and Federal Communication Commission (FCC) regulations. TheAmerican closed caption standard is EIA 608. The later is of particularinterest because many current generation televisions, especially largersizes, include a closed caption decoder, and thus require no externalhardware or connections, separate from the hardware and cabling forsupplying the video signal. See, TCC Tech Facts, Vols. 1-4,(www.wgbh.org, rev. September 1995) expressly incorporated herein byreference. The closed caption signal is distributed on Line 21 of thevertical blanking interval. The existing standard supports 480 bits/sec,with a potential increase to 9600 bits/sec in the forthcoming ATSCstandard.

Known systems provide a videoconferencing system which resides in a “settop box”, i.e., a stand-alone hardware device suitable for situation ontop of a television set, providing all of the necessary functionality ofa videoconferencing system employing the television as the display andpossibly audio speaker functions. These systems, however, do notintegrate the television functions, nor provide interaction between thevideo and videoconferencing systems. C-Phone Inc., Wilmington N.C.,provides a C-Phone Home product line which provides extensions to H.324and/or H.320 communications in a set-top box.

Other known videophone and videoconferencing devices are disclosed,e.g., in U.S. Pat. Nos. 5,600,646; 5,565,910; 5,564,001; 5,555,443;5,553,609; 5,548,322; 5,542,102; 5,537,472; 5,526,405; 5,509,009;5,500,671; 5,490,208; 5,438,357; 5,404,579; 5,374,952; 5,224,151;4,543,665; 4,491,694; 4,465,902; 4,456,925; 4,427,847; 4,414,432;4,377,729; 4,356,509; 4,349,701; 4,338,492; 4,008,376 and 3,984,638 eachof which is expressly incorporated herein by reference.

Known Web/TV devices (from Sony/Magnavox/Philips) allow use of atelevision to display alphanumeric data, as well as audiovisual data,but formats this data for display outside the television. In addition,embedded Web servers are also known. See, Richard A. Quinell, “WebServers in embedded systems enhance user interaction”, EDN, Apr. 10,1997, pp. 61-68, incorporated herein by reference. Likewise, combinedanalog and digital data transmission schemes are also known. See. U.S.Pat. No. 5,404,579.

A class of computing devices, representing a convergence of personalcomputers and entertainment devices, and which provide network access tothe Internet (a publicly available network operating over TCP/IP). ITUstandards for communications systems allow the selective addition ofdata, according to T.120-T.133, T.RES series of protocols, as well asHDLC, V.14, LAPM, to the videoconference stream, especially where excessbandwidth is available for upload or download.

A system may be provided with features enabling it to control aso-called smart house and/or to be a part of a security and/ormonitoring system, with imaging capability. These functions are providedas follows. As discussed above, various data streams may be integratedwith a videoconference data stream over the same physical link.Therefore, external inputs and outputs may be provided to the videophoneor videoconference terminal, which maybe processed locally and/ortransmitted over the telecommunications link. The local device, in thiscase, is provided with a continuous connection or an autodial function,to create a communications link as necessary. Therefore, heatingventilation and air conditioning control (HVAC), lighting, appliances,machinery, valves, security sensors, locks, gates, access points, etc.,may all be controlled locally or remotely through interfaces of thelocal system, which may include logic level signals, relays, serialports, computer networks, fiber optic interfaces, infrared beams, radiofrequency signals, transmissions through power lines, standard-typecomputer network communications (twisted pair, coaxial cable, fiberoptic cable), acoustic transmissions and other known techniques.Likewise, inputs from various devices and sensors, such as light oroptical, temperature, humidity, moisture, pressure, fluid level,security devices, radio frequency, acoustic, may be received andprocessed locally or remotely. A video and audio signal transmission mayalso be combined with the data signals, allowing enhanced remotemonitoring and control possibilities. This information, when transmittedthrough the telecommunication link, may be directed to another remoteterminal, for example a monitoring service or person seeking to monitorhis own home, or intercepted and processed at a central control unit oranother device. Remote events may be monitored, for example, on a closedcaption display mode of a television attached to a videophone.

While the preferred embodiments of the invention adhere to establishedstandards, the present invention also encompasses communications whichdeviate from or extend beyond such standards, and thus may engage inproprietary communications protocols, between compatible units.

Other References

In addition, the following patents are considered relevant to the datacompression and pattern recognition functions of the apparatus andinterface of the present invention and are incorporated herein byreference: U.S. Pat. Nos. 3,609,684; 3,849,760; 3,950,733; 3,967,241;4,025,851; 4,044,243; 4,100,370; 4,118,730; 4,148,061; 4,213,183;4,225,850; 4,228,421; 4,230,990; 4,245,245; 4,254,474; 4,264,924;4,264,925; 4,305,131; 4,326,259; 4,331,974; 4,338,626; 4,390,904;4,395,780; 4,420,769; 4,442,544; 4,449,240; 4,450,531; 4,468,704;4,491,962; 4,499,601; 4,501,016; 4,511,918; 4,543,660; 4,546,382;4,547,811; 4,547,899; 4,581,762; 4,593,367; 4,602,279; 4,630,308;4,646,250; 4,656,665; 4,658,429; 4,658,370; 4,660,166; 4,677,466;4,697,209; 4,672,683; 4,677,680; 4,682,365; 4,685,145; 4,695,975;4,710,822; 4,710,964; 4,716,404; 4,719,591; 4,731,863; 4,734,786;4,736,439; 4,739,398; 4,742,557; 4,747,148; 4,752,890; 4,653,109;4,760,604; 4,764,971; 4,764,973; 4,771,467; 4,773,024; 4,773,099;4,774,677; 4,775,935; 4,783,752; 4,783,754; 4,783,829; 4,789,933;4,790,025; 4,799,270; 4,802,103; 4,803,103; 4,803,736; 4,805,224;4,805,225; 4,805,255; 4,809,331; 4,809,341; 4,817,171; 4,817,176;4,821,333; 4,823,194; 4,829,453; 4,831,659; 4,833,637; 4,837,842;4,843,562; 4,843,631; 4,845,610; 4,864,629; 4,872,024; 4,876,731;4,881,270; 4,884,217; 4,887,304; 4,888,814; 4,891,762; 4,893,346;4,897,811; 4,905,162; 4,905,286; 4,905,296; 4,906,099; 4,906,940;4,908,758; 4,914,708; 4,920,499; 4,926,491; 4,930,160; 4,931,926;4,932,065; 4,933,872; 4,941,193; 4,944,023; 4,949,187; 4,956,870;4,958,375; 4,958,375; 4,964,077; 4,965,725; 4,967,273; 4,972,499;4,979,222; 4,987,604; 4,989,256; 4,989,258; 4,992,940; 4,995,078;5,012,334; 5,014,219; 5,014,327; 5,018,218; 5,018,219; 5,019,899;5,020,112; 5,020,113; 5,022,062; 5,027,400; 5,031,224; 5,033,101;5,034,991; 5,038,379; 5,038,390; 5,040,134; 5,046,121; 5,046,122;5,046,179; 5,047,867; 5,048,112; 5,050,223; 5,051,840; 5,052,043;5,052,045; 5,052,046; 5,053,974; 5,054,093; 5,054,095; 5,054,101;5,054,103; 5,055,658; 5,055,926; 5,056,147; 5,058,179; 5,058,180;5,058,183; 5,058,186; 5,059,126; 5,060,276; 5,060,277; 5,060,279;5,060,282; 5,060,285; 5,061,063; 5,063,524; 5,063,525; 5,063,603;5,063,605; 5,063,608; 5,065,439; 5,065,440; 5,065,447; 5,067,160;5,067,161; 5,067,162; 5,067,163; 5,067,164; 5,068,664; 5,068,723;5,068,724; 5,068,744; 5,068,909; 5,068,911; 5,076,662; 5,099,422;5,103,498; 5,109,431; 5,111,516; 5,119,507; 5,122,886; 5,130,792;5,132,992; 5,133,021; 5,133,079; 5,134,719; 5,148,497; 5,148,522;5,155,591; 5,159,474; 5,161,204; 5,168,529; 5,173,949; 5,177,796;5,179,652; 5,202,828; 5,220,420; 5,220,648; 5,223,924; 5,231,494;5,239,617; 5,247,347; 5,247,651; 5,259,038; 5,274,714; 5,283,641;5,303,313; 5,305,197; 5,307,421; 5,315,670; 5,317,647; 5,317,677;5,343,251; 5,351,078; 5,357,276; 5,381,158; 5,384,867; 5,388,198;5,390,125; 5,390,281; 5,410,343; 5,410,643; 5,416,856; 5,418,951;5,420,975; 5,421,008; 5,428,559; 5,428,727; 5,428,730; 5,428,774;5,430,812; 5,434,933; 5,434,966; 5,436,653; 5,436,834; 5,440,400;5,446,891; 5,446,919; 5,455,892; 5,459,517; 5,461,699; 5,465,308;5,469,206; 5,477,447; 5,479,264; 5,481,294; 5,481,712; 5,483,278;5,485,219; 5,485,518; 5,487,132; 5,488,425; 5,488,484; 5,495,292;5,496,177; 5,497,314; 5,502,774; 5,504,518; 5,506,768; 5,510,838;5,511,134; 5,511,153; 5,515,098; 5,515,099; 5,515,173; 5,515,453;5,515,471; 5,517,598; 5,519,452; 5,521,841; 5,521,984; 5,522,155;5,523,796; 5,524,065; 5,526,427; 5,535,302; 5,541,638; 5,541,662;5,541,738; 5,543,929; 5,544,254; 5,546,475; 5,548,667; 5,550,575;5,550,928; 5,550,965; 5,552,833; 5,553,221; 5,553,277; 5,554,983;5,555,495; 5,557,728; 5,559,548; 5,560,011; 5,561,649; 5,561,718;5,561,796; 5,566,274; 5,572,604; 5,574,845; 5,576,950; 5,579,471;5,581,658; 5,586,218; 5,588,074; 5,592,560; 5,574,845; 5,579,471;5,581,665; 5,581,800; 5,583,560; 5,586,025; 5,594,661; 5,594,911;5,596,705; 5,600,733; 5,600,775; 5,604,542; 5,604,820; 5,604,823;5,606,655; 5,611,020; 5,613,032; 5,614,940; 5,617,483; 5,617,565;5,621,454; 5,621,484; 5,621,579; 5,621,903; 5,625,715; 5,625,783;5,627,915; 5,634,849; 5,635,986; 5,642,434; 5,644,686; 5,644,735;5,654,771; 5,655,117; 5,657,397; 5,659,653; 5,659,368; 5,659,732;5,664,046; 5,668,897; 5,671,343; 5,671,411; 5,682,437; 5,696,964;5,701,369; 5,710,601; 5,710,833; 5,710,834; 5,715,400; 5,717,814;5,724,424; 5,724,472; 5,729,741; 5,734,893; 5,737,444; 5,740,274;5,745,126; 5,745,640; 5,745,710; 5,751,286; 5,751,831; 5,754,938;5,758,257; 5,761,655; 5,764,809; 5,767,893; 5,767,922; 5,768,421;5,768,426; 5,768,437; 5,778,181; 5,797,001; 5,798,785; 5,799,109;5,801,750; 5,801,753; 5,805,763; 5,809,471; 5,819,288; 5,828,809;5,835,087; 5,850,352; 5,852,823; 5,857,181; 5,862,260; H 331; and Re.33,316. The aforementioned patents, some of which are mentionedelsewhere in this disclosure, and which form a part of this disclosure,may be applied in known manner by those skilled in the art in order topractice various embodiments of the present invention.

The following scientific articles, some of which are discussed elsewhereherein, are understood by those skilled in the art and relate to thepattern recognition and image compression functions of the apparatus andinterface of the present invention:

“Fractal Geometry-Understanding Chaos”, Georgia Tech Alumni Magazine, p.16 (Spring 1986).

“Fractal Modelling of Biological Structures”, School of Mathematics,Georgia Institute of Technology (date unknown).

“Fractal Modelling of Real World Images”, Lecture Notes for Fractals:Introduction, Basics and Perspectives, Siggraph (1987).

“Fractals Yield High Compression”, Electronic Engineering Times, Sep.30, 1991, p. 39.

“Fractals-A Geometry of Nature”, Georgia Institute of TechnologyResearch Horizons, p. 9 (Spring 1986).

“Voice Recognition and Speech Processing”, Elektor Electronics,September 1985, pp. 56-57.

Aleksander, I., “Guide to Pattern Recognition Using Random-AccessMemories”, Computers and Digital Techniques, 2(1):29-40 (February 1979).

Anderson, F., W. Christiansen, B. Kortegaard, “Real Time, Video ImageCentroid Tracker”, Apr. 16-20, 1990.

Anson, L., M. Barnsley, “Graphics Compression Technology”, SunWorld, pp.43-52 (October 1991).

Appriou, A., “Interet des theories de l'incertain en fusion de donnees”,Colloque International sur le Radar Paris, 24-28 avril 1989.

Appriou, A., “Procedure d'aide a la decision multi-informateurs.Applications a la classification multi-capteurs de cibles”, Symposium del'Avionics Panel (AGARD) Turquie, 25-29 avril 1988.

Arrow, K. J., “Social choice and individual valves”, John Wiley and SonsInc. (1963).

Barnsley et al., “A Better Way to Compress Images”, Byte Magazine,January 1988.

Barnsley et al., “Harnessing Chaos For Images Systhesis”, ComputerGraphics, 22(4) (August 1988).

Barnsley et al., “Hidden Variable Fractal Interpolation Functions”,School of Mathematics, Georgia Institute of Technology, Atlanta, Ga.,30332, July, 1986.

Batchelor, B. G., “Pattern Recognition, Ideas in Practice”, PlenumPress, London and New York, (1978).

Batchelor, B. G., “Practical Approach to Pattern Classification”, PlenumPress, London and New York, (1974).

Bellman, R. E., L. A. Zadeh, “Decision making in a fuzzy environment”,Management Science, 17(4) (December 1970).

Bhatnagar, R. K., L. N. Kamal, “Handling uncertain information: a reviewof numeric and non-numeric methods”, Uncertainty in ArtificialIntelligence, L. N. Kamal and J. F. Lemmer, Eds. (1986).

Blair, D., R. Pollack, “La logique du choix collectif”, Pour la Science(1983).

Burr, D. J., “A Neural Network Digit Recognizer”, Proceedings of the1986 IEEE International Conference of Systems, Man and Cybernetics,Atlanta, Ga., pp. 1621-1625.

Caffery, B., “Fractal Compression Breakthrough for MultimediaApplications”, Inside, Oct. 9, 1991.

Carpenter, G. A., S. Grossberg, “The Art of Adaptive Pattern Recognitionby a Self-Organizing Neural Network”, IEEE Computer, March 1988, pp.77-88.

Casasent, D., et al., “General I and Q Data Processing on a MultichannelAO System”, Applied Optics, 25(18):3217-24 (Sep. 15, 1986).

Caudill, M., “Neural Networks Primer-Part III”, Al Expert, June 1988,pp. 53-59.

Chao, J. J., E. Drakopoulos, C. C. Lee, “An evidential reasoningapproach to distributed multiple hypothesis detection”, Proceedings ofthe 20th Conference on decision and control, Los Angeles, Calif.,December 1987.

Chao, T.-H.; Hegblom, E.; Lau, B.; Stoner, W. W.; Miceli, W. J.,“Optoelectronically implemented neural network with a waveletpreprocessor”, Proceedings of the SPIE—The International Society forOptical Engineering, 2026:472-82(1993).

Chen et al., “Adaptive Coding of Monochrome and Color Images”, November1977, pp. 1285-1292.

Cheong, C. K.; Aizawa, K.; Saito, T.; Hatori, M., “Adaptive edgedetection with fractal dimension”, Transactions of the Institute ofElectronics, Information and Communication Engineers D-II,J76D-II(11):2459-63 (1993).

Computer Visions, Graphics, and Image Processing, 1987, 37:54-115.

Computers and Biomedical Research 5, 388-410 (1972).

Cooper, L. N., “A Possible Organization of Animal Memory and Learning”,Nobel 24, (1973), Collective Properties of Physical Systems, pp.252-264.

Crawford et al., “Adaptive Pattern Recognition Applied To An ExpertSystem For Fault Diagnosis In Telecommunications Equipment”, pp. 10/1-8(Inspec. Abstract No. 86C010699, Inspec IEE (London) & IEE Coll. on“Adaptive Filters”, Digest No. 76, Oct. 10, 1985).

Danielsson, Erik, et al., “Computer Architectures for Pictorial Inf.Systems”, IEEE Computer, November, 1981, pp. 53-67.

Dempster, A. P., “A generalization of Bayesian inference”, Journal ofthe Royal Statistical Society, Vol. 30, Series B (1968).

Dempster, A. P., “Upper and lower probabilities induced by a multivaluedmapping”, Annals of mathematical Statistics, no. 38 (1967).

Denker, 1984 International Test Conf., October 1984, Philadelphia, Pa.,pp. 558-563.

Dubois, D., “Modeles mathematiques de l'imprecis et de l'incertain envue d'applications aux techniques d'aide a la decision”, DoctoralThesis, University of Grenoble (1983).

Dubois, D., N. Prade, “Combination of uncertainty with belief functions:a reexamination”, Proceedings 9th International Joint Conference onArtificial Intelligence, Los Angeles (1985).

Dubois, D., N. Prade, “Fuzzy sets and systems-Theory and applications”,Academic Press, New York (1980).

Dubois, D., N. Prade, “Theorie des possibilites: application a larepresentation des connaissances en informatique”, Masson, Paris (1985).

Duda, R. O., P. E. Hart, M. J. Nilsson, “Subjective Bayesian methods forrule-based inference systems”, Technical Note 124, ArtificialIntelligence Center, SRI International.

Dunning, B. B., “Self-Learning Data-Base For Automated FaultLocalization”, IEEE, 1979, pp. 155-157.

Farrelle, Paul M. and Jain, Anil K., “Recursive Block Coding-A NewApproach to Transform Coding”, IEEE Transactions on Communications, Com.34(2) (February 1986).

Fitzpatrick, J. M., J. J. Grefenstette, D. Van Gucht, “ImageRegistration by Genetic Search”, Conf. Proc., IEEE Southeastcon 1984,pp. 460-464.

Fua, P. V., “Using probability density functions in the framework ofevidential reasoning Uncertainty in knowledge based systems”, B.Bouchon, R. R. Yager, Eds. Springer Verlag (1987).

Gogoussis et al., Proc. SPIE Intl. Soc. Opt. Eng., November 1984,Cambridge, Mass., pp. 121-127.

Grossberg, S., G. Carpenter, “A Massively Parallel Architecture for aSelf-Organizing Neural Pattern Recognition Machine”, Computer Vision,Graphics, and Image Processing, 1987, 37, 54-115, 252-315.

Gullichsen, E., E. Chang, “Pattern Classification by Neural Network: AnExperiment System for Icon Recognition”, ICNN Proceeding on NeuralNetworks, March 1987, pp. IV-725-32.

Haruki, K. et al., “Pattern Recognition of Handwritten Phonetic JapaneseAlphabet Characters”, International Joint Conference on Neural Networks,Washington, D.C., January 1990, pp. 11-515 to 11-518.

Hayashi, Y., et al., “Alphanumeric Character Recognition Using aConnectionist Model with the Pocket Algorithm”, Proceedings of theInternational Joint Conference on Neural Networks, Washington, D.C. Jun.18-22, 1989, vol. 2, pp. 606-613.

Hayes, H. I.; Solka, J. L.; Priebe, C. E.; “Parallel computation offractal dimension”, Proceedings of the SPIE—The International Societyfor Optical Engineering, 1962:219-30 (1993).

Hinton et al., “Boltzmann Machines: Constraint Satisfaction Networksthat Learn”, Tech. Report CMU-CS-85-119, Carnegie-Mellon Univ, May 1984.

Hoare, F.; de Jager, G., “Neural networks for extracting features ofobjects in images as a pre-processing stage to pattern classification”,Proceedings of the 1992 South African Symposium on Communications andSignal Processing. COMSIG '92 (Cat. No. 92TH0482-0). Inggs, M. (Ed.), p.239-42 (1992).

Hopfield et al., “Computing with Neural Circuits: A Model”, Science,233:625-633 (8 Aug. 1986).

Hopfield, “Neural Networks and Physical Systems with Emergent CollectiveComputational Abilities”, Proc. Natl. Acad. Sci. USA, 79:2554-2558(April 1982).

Hopfield, “Neurons with graded response have collective computationalproperties like those of two-state neurons”, Proc. Natl. Acad. Sci. USA,81:3088-3092 (May 1984).

Hurtgen, B.; Buttgen, P., “Fractal approach to low rate video coding”,Proceedings of the SPIE—The International Society for OpticalEngineering, 2094(pt.1):120-31(1993).

Information Processing 71, North-Holland Publishing Company (1972) pp.1530-1533.

Ishizuka, M., “Inference methods based on extended Dempster and Shafer'stheory for problems with uncertainty/fuzziness”, New GenerationComputing, Ohmsha, Ltd, and Springer Verlag, 1:159-168 (1983).

Jackel, L. D., H. P. Graf, J. S. Denker, D. Henderson and I. Guyon, “AnApplication of Neural Net Chips: Handwritten Digit Recognition”, ICNNProceeding, 1988, pp. 11-107-15.

Jean, J. S. N., et al., “Input Representation and Output VotingConsiderations for Handwritten Numeral Recognition withBackpropagation”, International Joint Conference on Neural Networks,Washington, D.C., January 1990, pp. 1-408 to 1-411.

Jeffrey, R. J., “The logic of decision”, The University of ChicagoPress, Ltd., London (1983)(2nd Ed.).

Kaufmann, A., “Introduction a la theorie des sous-ensembles flous”, Vol.1, 2 et 3, Masson, Paris (1975).

Keeney, R. L., B. Raiffa, “Decisions with multiple objectives:Preferences and value tradeoffs”, John Wiley and Sons, New York (1976).

Kellman, P., “Time Integrating Optical Signal Processing”, Ph. D.Dissertation, Stanford University, 1979, pp. 51-55.

Kim, D. H.; Caulfield, H. J.; Jannson, T.; Kostrzewski, A.; Savant, G,“Optical fractal image processor for noise-embedded targets detection”,Proceedings of the SPIE—The International Society for OpticalEngineering, Vol: 2026 p. 144-9 (1993) (SPIE Conf: Photonics forProcessors, Neural Networks, and Memories 12-15 July 1993, San Diego,Calif., USA).

Kohonen, “Self-Organization & Memory”, Second Ed., 1988,Springer-Verlag, pp. 199-209.

Kortegaard, B. L., “PAC-MAN, a Precision Alignment Control System forMultiple Laser Beams Self-Adaptive Through the Use of Noise”, Los AlamosNational Laboratory, date unknown.

Kortegaard, B. L., “Superfine Laser Position Control Using StatisticallyEnhanced Resolution in Real Time”, Los Alamos National Laboratory,SPIE-Los Angeles Technical Symposium, Jan. 23-25, 1985.

Ksienski et al., “Low Frequency Approach to Target Identification”,Proc. of the IEEE, 63(12):1651-1660 (December 1975).

Kyburg, H. E., “Bayesian and non Bayesian evidential updating”,Artificial Intelligence 31:271-293 (1987).

LeCun, Y. et al., “Handwritten Digit Recognition: Applications of Neural.”, IEEE Comm. Magazine, November 1989, pp. 41-46.

LeCun, Y., “Connectionism in Perspective”, in R. Pfeifer, Z. Schreter,F. Fogelman, L. Steels (Eds.), 1989, “Generalization and Network DesignStrategies”, pp. 143-155.

Liepins, G. E., M. R. Hilliard, “Genetic Algorithms: Foundations &Applications”, Annals of Operations Research, 21:31-58 (1989).

Lin, H. K., et al., “Real-Time Screen-Aided Multiple-Image OpticalHolographic Matched-Filter Correlator”, Applied Optics, 21(18):3278-3286(Sep. 15, 1982).

Lippman, R. P., “An Introduction to Computing with Neural Nets”, IEEEASSP Magazine, April 1987, pp. 4-22.

Lippmann, R. P., “An Introduction to Computing with Neural Nets”, IEEEASSP Magazine, vol. 4(2):4-22 (April 1987).

Liu, Y., “Extensions of fractal theory”, Proceedings of the SPIE—TheInternational Society for Optical Engineering, 1966:255-68(1993).

Liu, Y., “Pattern recognition using Hilbert space”, Proceedings of theSPIE—The International Society for Optical Engineering, 1825:63-77(1992).

Mahalanobis, A., et al., “Minimum Average Correlation Energy Filters”,Applied Optics, 26(17):3633-40 (Sep. 1, 1987).

Martin, G. L. et al., “Recognizing Hand-Printed Letters and Digits UsingBackpropagation Learning”, Technical Report of the MCC, Human InterfaceLaboratory, Austin, Tex., January 1990, pp. 1-9.

McAulay, A. D., J. C. Oh, “Image Learning Classifier System UsingGenetic Algorithms”, IEEE Proc. of the National Aerospace & ElectronicsConference, 2:705-710 (1989).

Miller, R. K., Neural Networks ((c) 1989: Fairmont Press, Lilburn, Ga.),pp. 2-12 and Chapter 4, “Implementation of Neural Networks”, pp. 4-1 to4-26.

Molley, P., “Implementing the Difference-Squared Error Algorithm UsingAn Acousto-Optic Processor”, SPIE, 1098:232-239 (1989).

Molley, P., et al., “A High Dynamic Range Acousto-Optic Image Correlatorfor Real-Time Pattern Recognition”, SPIE, 938:55-65 (1988).

Mori, “Towards the construction of a large-scale neural network”,Electronics Information Communications Association Bulletin PRU 88-59,pp. 87-94.

Naik et al., “High Performance Speaker Verification .”, ICASSP 86,Tokyo, CH2243-4/86/0000-0881, IEEE 1986, pp. 881-884.

Ney, H., et al., “A Data Driven Organization of the Dynamic ProgrammingBeam Search for Continuous Speech Recognition”, Proc. ICASSP 87, pp.833-836, 1987.

Nilsson, N. J., The Mathematical Foundations of Learning Machines ((c)1990: Morgan Kaufmann Publishers, San Mateo, Calif.) and particularlysection 2.6 “The Threshold Logic Unit (TLU)”, pp. 21-23 and Chapter 6,“Layered Machines” pp. 95-114.

Ohsuga et al., “Entrainment of Two Coupled van der Pol Oscillators by anExternal Oscillation”, Biological Cybernetics, 51:225-239 (1985).

Omata et al., “Holonic Model of Motion Perception”, IEICE TechnicalReports, Mar. 26, 1988, pp. 339-346.

O'Neal et al., “Coding Isotropic Images”, November 1977, pp. 697-707.

Pawlicki, T. F., D. S. Lee, J. J. Hull and S. N. Srihari, “NeuralNetwork Models and their Application to Handwritten Digit Recognition”,ICNN Proceeding, 1988, pp. 11-63-70.

Perry et al., “Auto-Indexing Storage Device”, IBM Tech. Disc. Bulletin,12(8):1219 (January 1970).

Peterson, Ivars, “Packing It In”, Science News, 131(18):283-285 (May 2,1987).

Priebe, C. E.; Solka, J. L.; Rogers, G. W., “Discriminant analysis inaerial images using fractal based features”, Proceedings of the SPIE—TheInternational Society for Optical Engineering, 1962:196-208(1993).

Proceedings, 6th International Conference on Pattern Recognition 1982,pp. 152-136.

Psaltis, D., “Incoherent Electro-Optic Image Correlator”, OpticalEngineering, 23(1):12-15 (January/February 1984).

Psaltis, D., “Two-Dimensional Optical Processing Using One-DimensionalInput Devices”, Proceedings of the IEEE, 72(7):962-974 (July 1984).

Rahmati, M.; Hassebrook, L. G., “Intensity- and distortion-invariantpattern recognition with complex linear morphology”, PatternRecognition, 27 (4):549-68(1994) .

Reusens, E., “Sequence coding based on the fractal theory of iteratedtransformations systems”, Proceedings of the SPIE—The InternationalSociety for Optical Engineering, 2094(pt.1):132-40(1993).

Rhodes, W., “Acousto-Optic Signal Processing: Convolution andCorrelation”, Proc. of the IEEE, 69(1):65-79 (January 1981).

Rosenfeld, Azriel and Avinash C. Kak, Digital Picture Processing, SecondEdition, Volume 2, Academic Press, 1982.

Roy, B., “Classements et choix en presence de points de vue multiples”,R.I.R.O.-2eme annee-no. 8, pp. 57-75 (1968).

Roy, B., “Electre III: un algorithme de classements fonde sur unerepresentation floue des preferences en presence de criteres multiples”,Cahiers du CERO, 20(1):3-24 (1978).

Rumelhart, D. E., et al., “Learning Internal Representations by ErrorPropagation”, Parallel Distr. Proc.: Explorations in Microstructure ofCognition, 1:318-362 (1986).

Rumelhart, D. E., et al., Parallel Distributed Processing, ((c) 1986:MIT Press, Cambridge, Mass.), and specifically Chapter 8 thereof,“Learning Internal Representations by Error Propagation”, pp. 318-362.

Rutherford, H. G., F. Taub and B. Williams, “Object Identification andMeasurement from Images with Access to the Database to Select SpecificSubpopulations of Special Interest”, May 1986.

Rutter et al., “The Timed Lattice-A New Approach To Fast ConvergingEqualizer Design”, pp.VIII/1-5 (Inspec. Abstract No. 84C044315, InspecIEE (London) & IEE Saraga Colloquium on Electronic Filters, May 21,1984).

Sadjadi, F., “Experiments in the use of fractal in computer patternrecognition”, Proceedings of the SPIE—The International Society forOptical Engineering, 1960:214-22(1993).

Sakoe, H., “A Generalization of Dynamic Programming Based PatternMatching Algorithm Stack DP-Matching”, Transactions of the Committee onSpeech Research, The Acoustic Society of Japan, p. S83-23, 1983.

Sakoe, H., “A Generalized Two-Level DP-Matching Algorithm for ContinuousSpeech Recognition”, Transactions of the IECE of Japan, E65(11):649-656(November 1982).

Scharlic, A., “Decider sur plusieurs criteres. Panorama de l'aide a ladecision multicritere”, Presses Polytechniques Romandes (1985).

Schurmann, J., “Zur Zeichen und Worterkennung beim AutomatischenAnschriftenlesen”, Wissenschaftlichl, Berichte, 52(1/2) (1979).

Scientific American, “Not Just a Pretty Face”, March 1990, pp. 77-78.

Shafer, G., “A mathematical theory of evidence”, Princeton UniversityPress, Princeton, N.J. (1976).

Shimizu et al., “Principle of Holonic Computer and Holovision”, Journalof the Institute of Electronics, Information and Communication,70(9):921-930 (1987).

Shinan et al., “The Effects of Voice Disguise .”, ICASSP 86, Tokyo,CH2243-4/86/0000-0885, IEEE 1986, pp. 885-888.

Silverston et al., “Spectral Feature Classification and Spatial PatternRec.”, SPIE 201:17-26, Optical Pattern Recognition (1979).

Simpson, W. R., C. S. Dowling, “WRAPLE: The Weighted Repair AssistanceProgram Learning Extension”, IEEE Design & Test, 2:66-73 (April 1986).

Specht, IEEE Internatl. Conf. Neural Networks, 1:1525-1532 (July 1988),San Diego, Calif.

Sprageu, R. A., “A Review of Acousto-Optic Signal Correlators”, OpticalEngineering, 16(5):467-74 (September/October 1977).

Sprinzak, J.; Werman, M., “Affine point matching”, Pattern RecognitionLetters, 15(4):337-9(1994).

Stanley R. Sternberg, “Biomedical Image Processing”, IEEE Computer,1983, pp. 22-34.

Stewart, R. M., “Expert Systems For Mechanical Fault Diagnosis”, IEEE,1985, pp. 295-300.

Sugeno, M., “Theory of fuzzy integrals and its applications”, TokyoInstitute of Technology (1974).

Svetkoff et al., Hybrid Circuits (GB), No. 13, May 1987, pp. 5-8.

Udagawa, K., et al, “A Parallel Two-Stage Decision Method forStatistical Character Recognition .”, Electronics and Communications inJapan (1965).

Vander Lugt, A., “Practical Considerations for the Use of SpatialCarrier-Frequency Filters”, Applied Optics, 5(11):1760-1765 (November1966).

Vander Lugt, A., “Signal Detection By Complex Spatial Filtering”, IEEETransactions On Information Theory, IT-10, 2:139-145 (April 1964).

Vander Lugt, A., et al., “The Use of Film Nonlinearites in OpticalSpatial Filtering”, Applied Optics, 9(1):215-222 (January 1970).

Vannicola et al., “Applications of Knowledge Based Systems toSurveillance”, Proceedings of the 1988 IEEE National Radar Conference,20-21 Apr. 1988, pp. 157-164.

Vitols, “Hologram Memory for Storing Digital Data”, IBM Tech. Disc.Bulletin 8(11):1581-1583 (April 1966).

Wald, Sequential Analysis, Dover Publications Inc., 1947, pp. 34-43.

Wasserman, Philip D., “Neural Computing-Theory & Practice”, 1989, pp.128-129.

Willshaw et al., “Non-Holographic Associative Memory”, Nature,222:960-962 (Jun. 7, 1969).

Yager, R. R., “Entropy and specificity in a mathematical theory ofEvidence”, Int. J. General Systems, 9:249-260 (1983).

Yamada et. al., “Character recognition system using a neural network”,Electronics Information Communications Association Bulletin PRU 88-58,pp. 79-86.

Yamane et al., “An Image Data Compression Method Using Two-DimensionalExtrapolative Prediction-Discrete Sine Transform”, Oct. 29-31, 1986, pp.311-316.

Zadeh, L. A., “Fuzzy sets as a basis for a theory of possibility”, Fuzzysets and Systems, 1:3-28 (1978).

Zadeh, L. A., “Fuzzy sets”, Information and Control, 8:338-353 (1965).

Zadeh, L. A., “Probability measures of fuzzy events”, Journal ofMathematical Analysis and Applications, 23:421-427 (1968).

Zhi-Yan Xie; Brady, M., “Fractal dimension image for texturesegmentation”, ICARCV '92. Second International Conference onAutomation, Robotics and Computer Vision, p. CV-4.3/1-5 vol. 1, (1992).

Zhu, X., et al., “Feature Detector and Application to HandwrittenCharacter Recognition”, International Joint Conference on NeuralNetworks, Washington, D.C., January 1990, pp. 11-457 to 11-460.

The above-mentioned references are exemplary, and are not meant to belimiting in respect to the resources and/or technologies available tothose skilled in the art. Of course it should be realized that thehardware for implementing a system may be integrally related to thechoice of specific method or software algorithm for implementing thesystem, and therefore these together form a system. It is noted that inview of the present disclosure, it is within the skill of the artisan tocombine in various fashions the available methods and apparatus toachieve the advanced interface and control system of the presentinvention.

SUMMARY AND OBJECTS OF THE INVENTION

The present invention provides, according to one embodiment, an adaptiveuser interface which changes in response to the context, past historyand status of the system. The strategy employed preferably seeks tominimize, for an individual user at any given time, the search andacquisition time for the entry of data through the interface.

The interface may therefore provide a model of the user, which isemployed in a predictive algorithm. The model parameters may be static(once created) or dynamic, and may be adaptive to the user oralterations in the use pattern.

The present invention also provides a model-based pattern recognitionsystem, for determining the presence of an object within an image. Byproviding models of the objects within an image, the recognition processis relatively unaffected by perspective, and the recognition may takeplace in a higher dimensionality space than the transmitted media. Thus,for example, a motion image may include four degrees of freedom; x, y,chroma/luma, and time. A model of an object may include furtherdimensions, including z, and axes of movement. Therefore, the modelallows recognition of the object in its various configurations andperspectives.

A major theme of the present invention is the use of intelligent,adaptive pattern recognition in order to provide the operator with asmall number of high probability choices, which may be complex, withoutthe need for explicit definition of each atomic instruction comprisingthe desired action. The interface system predicts a desired action basedon the user input, a past history of use, a context of use, and a set ofpredetermined or adaptive rules.

Because the present invention emphasizes adaptive pattern recognition ofboth the input of the user and data which may be available, theinterface system proposes the extensive use of advanced signalprocessing and neural networks. These processing systems may be sharedbetween the interface system and the functional system, and therefore acontroller for a complex system may make use of the intrinsic processingpower available rather than requiring additional computing power,although this unification is not required. In the case where the userinterface employs common hardware elements, it is further preferred thatthe interface subsystem employ common models of the underlying datastructures on which the device functionally operates.

In fact, while hardware efficiency dictates common hardware for theinterface system and the operational routine, other designs may separatethe interface system from the operational system, allowing portabilityand efficient application of a single interface system for a number ofoperational systems. Thus, the present invention also proposes aportable human interface system which may be used to control a number ofdifferent devices. In this case, a web browser metaphor is preferred, asit has become a standard for electronic communications.

A portable interface may, for example, take the form of a personaldigital assistant or downloaded JAVA applet, with the data originatingin a web server. The data from a web server or embedded web server mayinclude a binary file, a generic HTML/XML file, or other data type. Theinterface receives the data and formats it based, at least in part, onparameters specific to the client or user. Thus, the presentation ofdata is responsive to the user, based on user preferences, as opposed tohardware limitations or compatibility issues. In a preferred embodiment,the data is transmitted separately from the presentation definition. Thepresentation definition, on the other hand, provides a set of parametersthat propose or constrain the data presentation. The user system alsoprovides a set of parameters that set preferences on presentation.Further, the data itself is analyzed for appropriate presentationparameters. These three sets of considerations are all inputs into a“negotiation” for an ultimate presentation scheme. Thus, thepresentation is adaptive to server parameters, user parameters, and thedata itself. For example, in a typical web-context, the color, size,typestyle, and layout of text may be modified based on theseconsiderations. Other factors that may be altered include frame size andlayout, size of hotspots, requirement for single or double clicks foraction, and the like.

The adaptive nature of the present invention derives from anunderstanding that people learn most efficiently through the interactiveexperiences of doing, thinking, and knowing. For ease-of-use,efficiency, and lack of frustration of the user, the interface of thedevice should be intuitive and self explanatory, providing perceptualfeedback to assist the operator in communicating with the interface,which in turn allows the operational system to receive a description ofa desired operation. Another important aspect of man-machine interactionis that there is a learning curve, which dictates that devices which areespecially easy to master become frustratingly elemental after continueduse, while devices which have complex functionality with many optionsare difficult to master and may be initially rejected, or the user stopsexploring. One such system which addresses this problem is U.S. Pat. No.5,005,084, expressly incorporated herein by reference. The presentinvention addresses these issues by determining the most likelyinstructions of the operator, and presenting these as easily availablechoices, by analyzing the past history data and by detecting the“sophistication” of the user in performing a function, based on allinformation available to it. The context of use may also be asignificant factor. The interface seeks to optimize the relevant portionof the interface adaptively and immediately in order to balance andoptimize the interface for both quantitative and qualitative factors.This functionality may greatly enhance the quality of interactionbetween man and machine, allowing a higher degree of overall systemsophistication to be tolerated and a greater value added than otherinterface designs. See, Commaford, C., “User-Responsive Software MustAnticipate Our Needs”, PC Week, May 24, 1993.

The present interface system analyzes data from the user, which may beboth the selections made by the user in context, as well as theefficiency by which the user achieves the selection. Thus, informationconcerning both the endpoints and time-dependent path of the process areconsidered and analyzed by the interface system.

The interface of the present invention may be advantageously applied toan operational system that has a plurality of functions, certain ofwhich are unnecessary or are rarely used in various contexts, whileothers are used with greater frequency. In such systems, thefunctionality use is usually predictable. Therefore, the presentinvention provides an optimized interface system which, upon recognizinga context, dynamically reconfigures the availability or ease ofavailability of functions and allow various subsets to be used through“shortcuts”. The interface presentation will therefore vary over time,use and the particular user.

The advantages to be gained by using an intelligent data analysisinterface for facilitating user control and operation of the system aremore than merely reducing the average number of selections or time toaccess a given function. Rather, advantages also arise from providing ameans for access and availability of functions not necessarilypreviously existing or known to the user, therefore improving theperceived quality and usefulness of the product. Further advantages overprior interfaces accrue due to the availability of pattern recognitionfunctionality as a part of the interface system.

In those cases where the pattern recognition functions are applied tolarge amounts of data or complex data sets, in order to provide asufficient advantage and acceptable response time, powerfulcomputational resources, such as advanced DSPs or neural networkprocessors are made available to the interface system. On the otherhand, where the data is simple or of limited scope, aspects of thetechnology may be easily implemented as added software functionality asimprovements of existing products having limited computationalresources.

The application of these technologies to multimedia systems provides anew model for performing image pattern recognition on multimedia dataand for the programming of applications including such data. The abilityof the interface of the present invention to perform abstractions andmake decisions regarding a closeness of presented data to selectioncriteria makes the interface suitable for use in a programmable control,i.e., determining the existence of certain conditions and taking certainactions on the occurrence of detected events. Such advanced technologiesmight be especially valuable for disabled users.

In a multimedia environment, a user often wishes to perform an operationon a multimedia data event. Past systems have required explicit indexingof images and events. The present technologies, however, allow an image,diagrammatic, abstract or linguistic description of the desired event tobe acquired by the interface system from the user and applied toidentify or predict the multimedia event(s) desired without requiring aseparate manual indexing or classification effort. These technologiesmay also be applied to single media data.

The interface system according to the present invention is not limitedto a single data source, and may analyze data from many differentsources for its operation. This data may be stored data or present in adata stream. Thus, in a multimedia system, there may be a real-time datastream, a stored event database, as well as an exemplar or modeldatabase. Further, since the device is adaptive, information relating topast experience of the interface, both with respect to exposure to datastreams and user interaction, is also stored. This data analysis aspectof the operation of the present interface system may be substantiallyprocessor intensive, especially where the data includes abstract orlinguistic concepts or images to be analyzed. Interfaces which do notrelate to the processing of such data may be implemented on simplerhardware. On the other hand, systems which handle complex data types maynecessarily include sophisticated processors, adaptable for use with theinterface system, thus minimizing the additional computing powernecessary in order to implement the interface according to the presentinvention. A portion of the data analysis may also overlap thefunctional analysis of the data for operation.

A fractal-based image processing system exemplifies one application ofthe technologies. A fractal-based system includes a database of imageobjects, which may be preprocessed in a manner which makes them suitablefor comparison to a fractal-transformed image representation of an imageto be analyzed. Thus, corresponding “fractal” transforms are performedon the unidentified image or a portion thereof and on an exemplar of adatabase. A degree of relatedness is determined in this “fractaltransform domain”, and the results used to identify objects within theimage. The system then makes decisions based on the information contentof the image, i.e. the objects contained therein.

The fractal-based image processing system presents many advantages.First, fractal-processed images may have dramatically reduced storagesize requirements as compared to traditional methods while substantiallyretaining information important for image recognition. The process maybe parallelized, and the exemplars may be multidimensional, furtherfacilitating the process of identifying a two-dimensional projection ofan object. The efficient storage of information allows the use ofinexpensive storage media, i.e., CD-ROM, or the use of an on-linedatabase through a serial data link, while allowing acceptablethroughput. See, Zenith Starsight Telecast brochure, (1994) ; U.S. Pat.No. 5,353,121, expressly incorporated herein by reference.

As applied to a multimedia database storage and retrieval system, theuser programs, through an adaptive user interface according to thepresent invention, the processing of data, by defining a criteria andthe actions to be taken based on the determination of the criteria. Thecriteria, it is noted, need not be of a predefined type, and in factthis is a particular feature of the present invention. A patternrecognition subsystem is employed to determine the existence of selectedcriteria. To facilitate this process, a database of image objects may bestored as two counterparts: first, the data is stored in a compressedformat optimized for normal use, such as human viewing on a videomonitor, using, e.g., MPEG-2 or Joint Photographic Experts Group (JPEG)compression; second, it is stored in a preprocessed and highlycompressed format adapted to be used with the pattern recognitionsystem. Because the preprocessed data is highly compressed and useddirectly by the pattern recognition system, great efficiencies instorage and data transmission are achieved. The image preprocessing mayinclude Fourier, DCT, wavelet, Gabor, fractal, or model-basedapproaches, or a combination thereof.

The potential significant hardware requirement for image processing andpattern recognition is counterbalanced by the enhanced functionalityavailable by virtue of the technologies. When applied to multimediadevices, the interface system allows the operator to define complexcriteria with respect to image, abstract or linguistic concepts, whichwould otherwise be difficult or impossible to formulate. Thus, theinterface system becomes part of a computational system that wouldotherwise be too cumbersome for use. It is noted that, in many types ofmedia streams, a number of “clues” are available defining the content,including close caption text, electronic program guides, simulcast data,related Internet web sites, audio tracks, image information, and thelike. The latter two data types require difficult processing in order toextract a semantic content, while the former types are inherentlysemantic data.

A pattern recognition subsystem allows a “description” of an “event”without explicit definition of the data representing the “event”. Thus,instead of requiring explicit programming, an operator may merely defineparameters of the desired “event”. This type of system is useful, forexample, where a user seeks a generic type of data representing avariety of events. This eliminates the need for preindexing orstandardized characterization of the data. The interface systemtherefore facilitates the formulation of a request, and then searchesthe database for data which corresponds to the request. Such preindexingor standardized characterization is extremely limiting with image andmultimedia data, because “a picture is worth a thousand words”, andwithout a priori knowing the ultimate search criteria, all possiblecriteria must be accounted for. Pattern recognition systems do notrequire initial translation of visual aspects into linguistic concepts,thus allowing broader searching capability. Of course, a patternrecognition system may be used in conjunction with other searchingschemes, to mutual advantage.

The pattern recognition functionality of the interface system is notlimited to multimedia data, and may be applied to data of almost anytype, e.g., real-time sensor data, distributed control, linguistic data,etc.

It is noted that, in consumer electronics and particularly entertainmentapplications, the reliability of the system need not be perfect, anderrors may be tolerable. On the other hand, in industrial controlapplications, reliability must be much higher, with fail-safe backupsystems in place, as well as advanced error checking. One way to addressthis issue is to allow the advanced user interface to propose an actionto the user, without actually implementing the action. However, in thiscase, the action and its proposed basis are preferably presented to theuser in a sophisticated manner, to allow the basis for the action to beindependently assessed by the user. Therefore, in a complex, multistepprocess, the user interface may be simplified by permitting a three stepprocess: the user triggers a proposed response, analyzes the proposaland rationale, and confirms the proposal. Therefore, single stepprocesses are inferior candidates for intelligent assistance.

Another notable aspect of the technologies is the contextual analysis.Often, multimedia data often includes a data component that closelycorresponds to a format of a search criteria. Thus, while a search mayseek a particular image, other portions of the datastream correlate wellwith the aspect of the image being searched, and may be analyzed byproxy, avoiding the need for full image analysis. The resultingpreselected reduced number of images may then be fully analyzed, ifnecessary. Thus, especially as with respect to consumer electronicsapplications, where absolute accuracy may not be required, theprocessing power available for pattern recognition need not besufficient for compete real-time signal analysis of all data. Thepresent invention therefore proposes use of a variety of available datain order to achieve the desired level functionality at minimum cost.

One aspect of the present invention therefore relates to a mechanism forfacilitating a user interaction with a programmable device. Theinterface and method of use of the present invention serves to minimizethe learning and searching times, better reflect users' expectations,provide better matching to human memory limits, be usable by bothnovices and experienced users, reduce intimidation of novice users bythe device, reduce errors and simplify the entering of programming data.The present invention optimizes the input format scheme for programmingan event-driven device, and can also be applied to many types ofprogrammable devices. Thus, certain human factors design concepts,heretofore unexploited in the design of consumer electronics devices andindustrial controls, have been incorporated, and new precepts developed.Background and theory of various aspects of the present invention isdisclosed in “AN IMPROVED HUMAN FACTORED INTERFACE FOR PROGRAMMABLEDEVICES: A CASE STUDY OF THE VCR”, Master's Thesis, Tufts University(Master of Sciences in Engineering Design, November, 1990, publiclyavailable January, 1991), by Linda I. Hoffberg. This thesis, and citedreferences, are incorporated herein by reference, and attached hereto asan appendix. Also referenced are: Hoffberg, Linda I., “Designing UserInterface Guidelines For Time-Shift Programming of a Video CassetteRecorder (VCR)”, Proc. of the Human Factors Soc. 35th Ann. Mtg. pp.501-504 (1991); and Hoffberg, Linda I., “Designing a ProgrammableInterface for a Video Cassette Recorder (VCR) to Meet a User's Needs”,Interface 91 pp. 346-351 (1991). See also, U.S. patent application Ser.No. 07/812,805, filed Dec. 23, 1991, incorporated herein by reference inits entirety, including appendices and incorporated references.

The present invention extends beyond simple predictive schemes whichpresent exclusively a most recently executed command or most recentlyopened files. Thus, the possible choices are weighted in amultifactorial method, e.g., history of use, context and system status,rather than a single simple criterion alone. Known simple predictivecriteria often exclude choices not previously selected, rather thanweighing these choices in context with those which have been previouslyselected. While the system according to the present invention mayinclude initial weightings, logical preferences or default settings,through use, the derived weightings are obtained adaptively based on ananalysis of the status, history of use and context. It is noted that notall of the possible choices need be weighted, but rather merely a subsetthereof.

For a given system, status, history of use and context may beinterrelated factors. For example, the status of the machine isdetermined by the prior use, while the status also intersects context.The intended meaning of status is information relating to a pathindependent state of the machine at a given point in time. History ofuse is intended to implicate more than the mere minimum instructions oractions necessary to achieve a given state, and therefore includesinformation unnecessary to achieve a given state, i.e., path dependentinformation. Context is also related to status, but rather isdifferentiated in that context refers to information relating to theenvironment of use, e.g., the variable inputs or data upon which theapparatus acts or responds. Status, on the other hand, is a narrowerconcept relating more to the internal and constant functionality of theapparatus, rather than the particularities of its use during specificcircumstances.

U.S. Pat. No. 5,187,797 relates to a machine interface system havinghierarchical menus, with a simple (three button) input scheme. Thechoice(s) presented relate only to the system status, and not theparticular history of use employed to obtain the system status nor thecontext of the choice. This system has a predetermined hierarchical menustructure, which is invariant with usage. The goal of this interfacesystem is not to provide a learning interface, but rather to teach theuser about or conform the user to the dictates of the predetermined andinvariant interface of the device. While many types of programmabledevices are known to exist, normally, as provided in U.S. Pat. No.5,187,797, instructions are entered and executed in a predeterminedsequence, with set branch points based on input conditions or theenvironment. See also U.S. Pat. Nos. 4,878,179, 5,124,908, and5,247,433.

An aspect of the present invention provides a device having apredetermined or a generic style interface upon initial presentation tothe user, with an adaptive progression in which specialized featuresbecome more easily available to a user who will likely be able to makeuse of them, while unused features are or remain “buried” within theinterface. The interface also extracts behavioral information from theuser and to alter the interface elements to optimize the efficiency ofthe user.

A videocassette recorder is a ubiquitous example of a programmabledevice, and therefore forms the basis of much of the discussion herein.It should, of course, be realized that many of the aspects of thepresent invention could be applied by one of ordinary skill in the artto a variety of controls having human interfaces, and that these otherapplications are included within the scope of the present invention.

The VCR apparatus typically involves a remote control entry device, andthe interface of the present invention contains a graphical interfacedisplayed for programming programmable devices. This aspect of thepresent invention seeks more accurate programming through the use ofprogram verification to ensure that the input program is both valid andexecutable. Thus, it has a mechanism to store and check to verify thatthere are no conflicting programs. An apparatus according to the presentinvention can be connected, for example, to any infrared programmabledevice in order to simplify the programming process. By way of exampleonly, an improved VCR interface forms the basis of a disclosed example.It is, of course, realized that the present method and apparatus may beapplied to any programmable controller, i.e., any device which monitorsan event or sensor and causes an event when certain conditions orparameters are met, and may also be used in other programmingenvironments, which are not event driven. While the present interface ispreferably learning and adaptive, it may also detect events and makedecisions based on known or predetermined characteristics. Where anumber of criteria are evaluated for making a decision, conflicts amongthe various criteria are resolved based on a strength of an evaluatedcriteria, a weighting of the criteria, an interactivity functionrelating the various criteria, a user preference, either explicitly orimplicitly determined, and a contextual analysis. Thus, a user overrideor preference input may be provided to assist in resolving conflicts.

The present invention may incorporate an intelligent program recognitionand characterization system, making use of any of the available cues,which allows an intelligent determination of the true nature of thebroadcast and therefore is able to make a determination of whetherparameters should be deemed met even with an inexact match to thespecified parameters. Therefore, in contradistinction with VPV, thepresent invention provides, for example, intelligence. The VPV is muchmore like the “VCR Plus” device, known to those skilled in the art,which requires that a broadcast be associated with a predetermined code,with the predetermined code used as a criteria for initiating recording.Some problems with VCR Plus include identification of the codes whichidentify channel and time, post scheduling changes, incorrect VCR clocksetting, and irregular schedules. VCR Plus also is limiting with respectto new technologies and cable boxes.

The videotext signal of the prior art includes a digitally encoded textmessage that may be displayed in conjunction with the displayed image,similar to the closed caption system. The aforementioned West Germansystem demonstrates one way in which the transmitted signal may bereceived by a device and interpreted to provide useful information otherthan the transmitted program itself. However, the prior art does notdisclose how this signal may be used to index and catalog the contentsof a tape, nor does it disclose how this signal may be used to classifyor interpret the character of the broadcast. In other words, in oneembodiment of the present invention, the videotext or closed captionsignal is not only interpreted as a literal label, as in the prior art,but is also further processed and analyzed to yield data about thecontent of the broadcast, other than merely an explicit identificationof the simultaneously broadcast information.

Beyond or outside the visible region of an U.S. National TelevisionStandards Committee (NTSC) broadcast video frame are a number of scanlines which are dedicated to presenting digital information, rather thananalog picture information. Various known coding schemes are availablefor transmitting and receiving information in this non-viewing portionof the video transmission, and indeed standard exist defining thecontent of these information fields. Of course, various othertransmission schemes provide a format for transmitting data. Forexample, standard frequency modulation (FM) transmissions may beassociated with digital data transmissions in a subcarrier. Likewise,satellite transmissions may include digital data along with an audiodata stream or within a video frame, which may be in analog format ordigitally encoded.

Cable systems may transmit information either in the broadcast band orin a separate band. HDTV schemes also generally provide for thetransmission of digital data of various sorts. Thus, known audio andvideo transmission systems may be used, with little or no modificationsto provide enhanced functionality, according to the present invention.It is therefore possible to use known and available facilities fortransmitting additional information relating to the broadcastinformation, in particular, the characteristics of the video broadcast,and doing so could provide significant advantages, used in conjunctionwith the interface and intelligent pattern recognition controller of thepresent invention. If this information were directly available, therewould be a significantly reduced need for advanced image recognitionfunctions, such advanced image recognition functions requiring costlyhardware devices, while still maintaining the advantages of the presentinvention.

It is noted, however, that the implementation of a system in whichcharacterization data of the broadcast is transmitted along therewithmight require a new set of standards and the cooperation ofbroadcasters, as well as possibly the government regulatory and approvalagencies. The present invention does not require, in all of its aspects,such standardization, and therefore may advantageously implementsubstantial data processing locally to the receiver. It is neverthelesswithin the scope of the invention to implement such a broadcast systemwith broadcast of characterization data in accordance with the presentinvention. Such broadcast characterization data may includecharacterizations as well as preprocessed data useful for characterizingaccording to flexible criteria in the local receiving device.

According to the present invention, if such characterizations arebroadcast, they may, as stated above, be in band or out of band, e.g.,making use of unused available spectrum bandwidth within the NTSCchannel space, or other broadcast system channel space, or may be“simulcast” on a separate channel, such as an FM sideband or separatetransmission channel. Use of a separate channel would allow a separateorganization, other than the network broadcasters, to provide thecharacterization data for distribution to users of devices that make useof the present intelligent system for controlling a VCR or otherbroadcast information processing device. Thus, the characterizationgenerating means need not be directly linked to the local user machinein order to fall within the scope of the present invention. The presentinvention also provides a mechanism for copyright holders or otherproprietary interests to be protected, by limiting access to informationbe encryption or selective encryption, and providing an accountingsystem for determining and tracking license or broadcast fees.

Research has been performed relating to VCR usability, technology,implementation, programming steps, current technology, input devices,and human mental capacity. This research has resulted in a new paradigmfor the entry of programming data into a sequential program executiondevice, such as a VCR, by casual users.

Four major problems in the interfaces of VCRs were found to exist. Thefirst is that users spend far too much time searching for necessaryinformation, which is necessary in order to complete the programmingprocess. Second, many people do not program the VCR to record at a latertime (time-shift) frequently, and thus forget the programming steps inthe interim, i.e., the inter-session decay of the learning curve issignificant. Third, the number of buttons on many remote control deviceshas become overwhelming. Fourth, people have become reluctant to operateor program VCRs because of their difficult operation. It was found that,by minimizing the learning and searching times, the user's programmingtime and frustration level can be greatly reduced. If VCRs are easier toprogram, users might program them more frequently. This would allow moreefficiency and flexibility in broadcast scheduling, especially latenight for time shift viewing. The present invention therefore providesan enhanced VCR programming interface having a simplified informationstructure, an intuitive operational structure, simplified control layoutand enhanced automated functionality.

A new class of consumer device has been proposed, which replaces thevideotape of a traditional videotape recorder with a random-accessstorage device, such as a magnetic hard disk drive. Multimedia data isconverted through a codec (if necessary), and stored in digital form.Such systems are proposed by Tivo, Inc., Philips Electronics (PersonalTV), Replay Networks, Inc. and Metabyte, Inc. Some of these systemsemploy a user preference based programming/recording method similar tothat of the present invention.

In these systems, typically a content descriptive data stream formulatedby human editors accompanies the broadcast or is available forprocessing and analysis. Based on a relation of the user preferences,which may be implied by actual viewing habits or input through simpleaccept/veto user feedback, selected media events may be recorded.However, such systems rely on a correspondence between the factors ofinterest to users and those encoded in the data stream, e.g., a “programguide”. This is not always the case. However, where the available datadescribing the program maps reasonably well into the user preferencespace, such a system may achieve acceptable levels of performance, orstated otherwise, the program material selected by the system will beconsidered acceptable.

One particular aspect of these time-shifting consumer media recordingdevices is how they deal with advertising materials which accompanyprogram material. In many instances, the user seeks to avoid“commercials”, and the device may be programmed to oblige. However, assuch devices gain wider acceptance, advertisers will be reluctant tosubsidize broadcasts. Therefore, an advertising system may be integratedinto the playback device which seeks to optimize the commercial messagespresented to a viewer. By optimizing the messages or advertisements, theviewer is more receptive to the message, and economic implicationsensue. For example, a viewer may be compensated, directly or indirectly,for viewing the commercials, which may be closely monitored and audited,such as by taking pictures of the audience in front of a “set-top box”.The acquired data, including viewer preferences, may be transmitted backto commercial sponsors, allowing detailed demographic analysis.

In order to ensure privacy, the preference information and/or images maybe analyzed by a proxy, with the raw data separated from the commercialusers of such data. Thus, for example, the particular users of a systemmay register their biometric characteristics, e.g., face. Thereafter,the imager captures facial images and correlates these with its internaldatabase. The image itself therefore need not be stored or transmitted.Viewer preferences and habits, on the other hand, likely must betransmitted to a central processing system for analysis.

Because the system is intelligent, copy protection and royaltyaccounting schemes may readily be implemented. Thus, broadcasters andcontent providers may encode broadcasts in such a way as to control theoperation of the consumer device. For example, an IEEE-1394-typeencryption key support/copy protection or DIVX scheme may beimplemented. Further, certain commercial sponsors may be able to avoiddeletion of their advertisement, while others may allow truncation. Theacceptability of this to the consumer may depend on subsidies. In otherwords, an company is willing to pay for advertising. Instead of payingfor placements directly to the media, a portion is paid to a serviceprovider, based on consumer viewing. The media, on the other hand, mayseek to adopt a pay-per-view policy, at least with respect to theservice provider, in lieu of direct advertising revenues. The serviceprovider will account to both advertisers and content providers for use.With sufficient viewing of commercials, the entire service charge for asystem might be covered for a user. On the other hand, a viewer mightprefer to avoid all commercials, and not get the benefit of a subsidy.The service provider performs the economically efficient function ofdelivering optimized, substituted commercials for the almost randomcommercials which flood the commercial broadcast networks, and thus canaccrue greater profits, even after paying content providers a reasonablefee. An advertiser, by selecting a particular audience, may pay lessthan it would otherwise pay to a broadcaster. The content providers mayalso charge more for the privilege of use of their works.

As stated above, the content may be copy protected by the use ofencryption and/or lockout mechanisms. Thus, by providing an alternativeto an analog VCR, a full end-to-end encrypted signal may be provided,such as that proposed for the IEEE-1394 copy protection scheme. Becauseenhanced recording capabilities are provided to the consumer, theacceptance will be high. Because of the encryption, lack of portabilityand continued royalty accounting, content provider acceptance will alsolikely be high.

The user interface concepts according to the present invention areeasily applied to other special purpose programmable devices, and alsoto general purpose programmable devices wherein the programming paradigmis event-driven, as well as other programming systems. It should also benoted that it is within the scope of the present invention to provide animproved interface and programming environment for all types ofprogrammable devices, and in this regard, the present inventionincorporates adaptive features which optimize the programmingenvironment for both the level of the user and the task to beprogrammed.

In optimizing the interface, four elements are particularly important:the input device, the display format, the sequence of the programmingoperation, and the ability of the device to properly interpret the inputas the desired program sequence.

The present invention proceeds from an understanding that an absence ofuser frustration with respect to a programmable consumer or industrialdevice or interface, may be particularly important with respect toachieving the maximum potential functionality thereof. The interfacemust be designed to minimize the user's frustration level. This can beaccomplished by clearly furnishing the possible choices, presenting thedata in a logical sequence, and leading the user through the stepsnecessary to program the device.

When applied to other than audiovisual and/or multimedia application,the pattern recognition function may be used to control the execution ofa program or selectively control execution of portions of the software.For example, in a programmable temperature controller application, asensor or sensor array could be arranged to detect a “door opening”. Onthe occurrence of the door opening, the system would recognize thispattern, i.e. a mass of air at a different temperature entering theenvironment from a single location, or a loss of climate controlled airthrough a single location. In either event, the system would takeappropriate action, including: halt of normal climate control and imposea delay until the door is closed; after closure, set a time constant formaintenance of a steady state of the replaced air with the climatecontrolled air; based on the actual climatic condition afterassimilation, or a predicted climatic condition after assimilation,begin a climate compensation control; optionally, during the dooropening, control a pressure or flow of air to counterbalance the normalflow through the door, by using a fan or other device. The climate maydiffer in temperature, humidity, pollutants, or the like, andappropriate sensors may be employed.

The present invention also allows a dynamic user preference profiledetermination based on explicit or implicit desires, e.g., moods, whichassist in processing data to make decisions which conform to the userpreference at a given point in time. For example, voice patterns, skintemperature, heat pulse rate, external context, skin resistance(galvanic skin response), blood pressure, stress, as determined by EMG,EEG or other known methods, spontaneous motor activity or twitching, maybe detected in order to determine or infer a user mood, which may beused as a dynamic influence on the user preference. These dynamicinfluences are preferably stored separately from static influences ofthe preferences, so that a resultant determined preference includes adynamic influence based on a determined mood or other temporally varyingfactor and a static influence associated with the user.

When a group of people are using the system simultaneously, the systemmust make a determination of a composite preference of the group. Inthis case, the preferences of the individuals of the group, if known,may be correlated to produce an acceptable compromise. Where individualpreferences are not a priori known, individual or group “interviews” maybe initially conducted to assist in determining the best composite grouppreference.

It is therefore an object according to the present invention to providea radio receiver or video receiver device, having a plurality ofdifferent available program sources, determining a program preferencefor one or more individuals subject to a presented program, comparingthe determined program preference and a plurality of different programsources, and selects at least one program based on the comparison.

In formulating a group preference, individual dislikes may be weightedmore heavily than likes, so that the resulting selection is tolerable byall and preferable to most group members. Thus, instead of a best matchto a single preference profile for a single user, a group systemprovides a most acceptable match for the group. It is noted that thismethod is preferably used in groups of limited size, where individualpreference profiles may be obtained, in circumstances where the groupwill interact with the device a number of times, and where the subjectsource program material is the subject of preferences. Where largegroups are present, demographic profiles may be employed, rather thanindividual preferences. Where the device is used a small number of timesby the group or members thereof, the training time may be verysignificant and weigh against automation of selection. Where the sourcematerial has little variety, or is not the subject of strongpreferences, the predictive power of the device as to a desiredselection is limited.

The present invention provides a system and method for making use of theavailable broadcast media forms for improving an efficiency of matchingcommercial information to the desires and interests of a recipient,improving a cost effectiveness for advertisers, improving a perceivedquality of commercial information received by recipients and increasingprofits and reducing required information transmittal by publishers andmedia distribution entities.

This improved advertising efficiency is accomplished by providing asystem for collating a constant or underlying published content workwith a varying, demographically or otherwise optimized commercialinformation content. This commercial information content therefore neednot be predetermined or even known to the publisher of the underlyingworks, and in fact may be determined on an individual receiver basis. Itis also possible to integrate the demographically optimized informationwithin the content. For example, overlays in traditional media, andelectronic substitutions or edits in new media, may allow seamlessintegration. The content alteration need not be only based on commercialinformation, and therefore the content may vary based on the user orrecipient.

U.S. Pat. No. 5,469,206, expressly incorporated herein by reference,relates to a system that automatically correlates user preferences withelectronic shopping information to create a customized database for theuser.

Therefore, the granularity of demographic marketing may be very fine, ona receiver-by-receiver basis. Further, the accounting for advertiserswill be more accurate, with a large sample and high quality information.In fact, in a further embodiment, an interactive medium may be usedallowing immediate or real time communication between recipient andadvertiser. This communication may involve the Internet, privatenetworks or dial-up connections. Because the commercial messages areparticularly directed to recipients, communication with each selectedrecipient is more valuable to an advertiser and that advertiser iswilling to pay more for communication with each selected recipient.Recipients may therefore be selected to receive the highest valuedappropriate commercial message(s). Thus, advertisers will tend to payless and media producers will gain more revenues. Recipients will gainthe benefit of selected and appropriate media, and further, may providefeedback for determining their preferences, which will likely correspondwith their purchasing habits. Thus, the recipient will benefit byreceiving optimized information.

Likewise, a recipient may place a value on receiving certaininformation, which forms the basis for “pay-per-view” systems. In thiscase, the recipient's values may also be considered in defining theprogramming.

This optimization is achieved by providing a device local to therecipient which selectively presents commercial information to therecipient based on characteristics individual to the recipient, whichmay be input by the recipient, the publisher, the advertiser, and/orlearned by the system based on explicit or implicit feedback. The localdevice either has a local memory for advertising materials, or atelereception link for receiving commercial information forpresentation, either on a real time basis or stored for laterpresentation. In a further embodiment, a user may control the contentand/or commercial information received. In this case, the accountingsystem involves the user's account, and, for example, the recipient maybe denied the subsidy from the commercial advertiser, and pay for theprivilege of commercial free content.

It is also possible to employ the methods and systems according to thepresent invention to create a customized publication, which may bedelivered physically to the recipient, for example as print media,facsimile transmission, e-mail, R-CD-ROM, floppy disk, or the like,without having a device local to the consumer.

It is noted that this system and method is usable for both real timemedia, such as television, radio and on-line telecommunication, as wellas manually distributed periodicals, such as newspapers, magazines,CD-ROMs, diskettes, etc. Therefore, the system and method according tothe present invention includes a set of related systems with varyingdetails of implementation, with the underlying characteristic ofoptimization of variable material presentation at the recipient levelrather than the publisher level.

The system and method according to the present invention preferablyincludes an accounting system which communicates information relating toreceipt of commercial advertising information by a recipient to acentral system for determination of actual receipt of information. Thisfeedback system allows verification of receipt and reduces thepossibility of fraud or demographic inaccuracies.

The accounting system, for example, may place value on the timeslot,associated content, the demographics of the user, user's associatedvaluation, competition for placement, past history (number ofimpressions made to same recipient) and exclusivity.

A preferred embodiment includes a subscription television system havinga plurality of received channels. At least one of these channels isassociated with codes to allow determination of content from variablesegments. It is also possible to identify these variable segmentswithout these codes, although the preferred system includes use of suchcodes. These codes also allow simple identification of the content foraccounting purposes. Upon detection of a variable segment, a commercialadvertisement is selected for presentation to the recipient. Thisvariable segment is selected based on the characteristics of therecipient(s), the history of use of the device by the recipient(s), thecontext of use, the arrangements made by the commercial informationprovider(s) for presentation of information, and the availability ofinformation for presentation. Other factors may include theabove-mentioned accounting system factors. Typically, the local devicewill include a store of commercial information, downloaded or otherwisetransmitted to the recipient (e.g., a CD-ROM or DVD with MPEG-2compressed images). A telecommunication link may also be provided tocontrol the process, provide parameters for the presentation or theinformation itself. This telecommunication link may be provided throughthe public telephone network, Internet, private network (real orvirtual) cable network, or a wireless network, for example. Generally,the underlying work will have a gap of fixed length, so that thecommercial information must be selected to fit in this gap. Where thegap is of variable length, such as might occur in live coverage, thecommercial information is interrupted or the underlying work bufferedand delayed to prevent loss. Thus, the presentation to the user isconstructed from pieces, typically at the time of presentation, and mayinclude invariable content, variable content, invariable messages,variable messages, targeted content and/or messages, and hypervariablecontent. Hypervariable content includes, for example, transitionmaterial selected based on the stream of information present, and otherpresentations which my optionally include useful information which areindividualized for the particular recipient or situation.

According to another embodiment, a recording, such as on a videotape, isretained by a recipient which includes proprietary content. This mayinclude a commercial broadcast, a private broadcast, or distributedmedia. In the case of a commercial broadcast, some or all of thecommercial advertising or other time-sensitive information is old and/orstale. Therefore, in operation, this old or time sensitive informationis eliminated and substituted with new and/or different information.Thus, the presentation system freshens the presentation, editing andsubstituting where necessary.

By such a method, content distributed even through private channels mayinclude advertisements, and thus be subsidized by advertisers. Theadvertisements and other added content are generally more acceptable tothe audience because they are appropriately targeted.

For example, where the broadcaster has a high degree of control over theinitial broadcast, e.g., pay per view under license, or where thebroadcaster may claim substantial continuing rights in the work afterrecording, the enforcement of a proprietary replay system may beaccepted. For example, a work is broadcast as an encrypted digital datastream, with selective decryption at the recipient's receiver, underlicense from the broadcaster. In this case, a recording system isprovided which retains the encryption characteristics, ensuring theintegrity of the accounting process. During presentation of the recordedwork, commercial information is appropriately presented to the recipientduring existing or created gaps, or in an associated output separatefrom the content presentation. The recipient, as a result, receives thebenefit of the original subsidy, or may receive a new subsidy.

Therefore, similar to the known DIVX system, an encrypted media may bemass distributed, which requires authorization for display. Instead,however, of requiring the recipient to pay for the initial andsubsequent displays of the content, the player integrates advertisingcontent into the output, which may vary based on the audience, time andpast history, as well as other factors discussed herein. Given theinteractive and variable nature of the presentation, the user oraudience may even veto (“fast forward through”) a particular commercial.In this case, the use may have to account for a fee, or otheradvertisers may tack up the slack. The veto provides informationregarding the desires of the viewer, and may be used to help selectfuture messages to the displayed or presented.

According to another embodiment, a radio transmission/reception systemis provided which broadcasts content, an overlay track and variablecommercial information. The invariant works are preferably prerecordedmusic. The overlay track is preferably a “DJ”, who provides informationregarding the invariant works, commercial information or news. Thecommercial information in this instance therefore refers to prerecordedsegments. In this instance, the goal is to allow the invariant works tobe received by the recipient and presented with improved optimization ofthe commercial information content and other messages presented at thetime of output. Further, this system allows optimization of thepresentation of the invariant portions as well, i.e., the commercialinformation and the program content may be independently selected at thereceiver, with appropriate accounting for commercial subsidy. In amobile receiver, it is preferable to include as a factor in theselection of commercial information a location of the receiver, as mightbe obtained from a GPS system, cellular location system, intelligenthighway system or the like. This would allow geographically appropriateselection of commercial information, and possibly overlay information aswell, e.g., traffic reports.

Another embodiment according to the present invention provides ahypertext linked media or multimedia environment, such as HTML/WorldWide Web, wherein information transmitted and/or displayed is adaptivelyselected based on the particular user or the user's receiving system.Thus, various elements may be dynamically substituted during use.

Therefore, it is an object according to the present invention to provideadaptive man-machine interfaces, especially computer graphic userinterfaces, which are ergonomically improved to provide an optimizedenvironment. Productivity of computer operators is limited by the timenecessary to communicate a desired action through the user interface tothe device. To reduce this limitation, most likely user actions arepredicted and presented as easily available options. The technologiesalso extend beyond this core theme in many differing ways, depending onthe particular application.

The system also provides an intelligent, adaptive pattern recognitionfunction in order to provide the operator with a small number of highprobability choices, which may be complex, without the need for explicitdefinition of each atomic instruction comprising the desired action. Theinterface system predicts a desired action based on the user input, apast history of use, and a context of use.

In yet another embodiment, a present mood of a user is determined,either explicitly or implicitly, and the device selects program materialthat assists in a desired mood transition. The operation of the devicemay additionally acquire data relating to an individual and therespective moods, desires and characteristics, altering the pathprovided to alter the mood based on the data relating to the individual.As stated above, in a group setting, a most acceptable path is presentedrather than a most desirable path as presented for an individual.

In determining mood, a number of physiologic parameters may be detected.In a training circumstance, these set of parameters are correlated witha temporally associated preference. Thus, when a user inputs apreference into the system as feedback, mood data is also obtained.Invariant preferences may be separated, and analyzed globally, withoutregard for temporal variations, while varying preferences are linkedwith information regarding the surrounding circumstances and stored. Forexample, the preference data may be used to train a neural network,e.g., using backpropagation of errors or other known methods. The inputsto the neural network include available data about surrounding context,such as time, environmental brightness, and persons present; sourceprogram choices, which may be raw data, preprocessed data, andabstracted data; explicit user input; and, in this embodiment, moodparameters, which may be physiological or biometric data, voice pattern,or implicit inputs. An example of an implicit input is an observation ofa man-machine interaction, such as a video game. The manner in which aperson plays a video game or otherwise interacts with a machine mayprovide valuable data for determining a mood or preference.

According to one embodiment of the invention, the image is preprocessedto decompose the image into object-elements, with variousobject-elements undergoing separate further processing. For example,certain backgrounds may be aesthetically modeled using simple fractalequations. While, in such circumstances the results may be inaccurate inan absolute sense, they may be adequate in a performance sense. Faces,on the other hand, have common and variable elements. Therefore, afacial model may be based on parameters having distinguishing power,such as width between eyes, mouth, shape of ears, and other proportionsand dimensions. Thus, along with color and other data, a facial imagemay be stored as a reference to a facial model with the distinguishingparameters for reconstruction. Such a data processing scheme may producea superior reconstructed image and allow for later recognition of theface, based on the stored parameters in reference to the model.Likewise, many different elements of an image may be extracted andprocessed in accordance with specific models to produce differentiatingparameters, wherein the data is stored as a reference to the particularmodel along with the particular data set derived from the image. Such aprocessing scheme allows efficient image storage along with ease ofobject recognition, i.e., distinction between objects of the same class.This preprocessing provides a highly asymmetric scheme, with a fargreater processing complexity to initially process the image than tosubsequently reconstruct or otherwise later employ the data.

By employing a model-based object extraction system, the availablebandwidth may be efficiently used, so that objects which fall within thescope of an available model may be identified with a modelidentification and a series of parameters, and objects not within thescope of a model may be allocated a comparatively greater bandwidth forgeneral image description, e.g., JPEG, MPEG-1/MPEG-2, wavelet, standardfractal image compression (FIC), or other image processing schemes. In aworst case, therefore, the bandwidth required will be only slightlygreater than that required for a corresponding standard method, due onlyto the additional overhead to define data types, as necessary. However,by employing a model based-object decomposition processing system,recognized elements may be described using only a small amount of dataand a greater proportion of data used to describe unrecognized elements.Further, the models available may be dynamically updated, so that, asbetween a communicating transmitted and receiver, retransmission ofunrecognized elements will be eliminated as a model is constructed.

Where image processing systems may produce artifacts and errors, anerror minimization function may also be provided which compares anoriginal image with a decomposed-recomposed image and produces an errorfunction which allows correction for these errors. This error functionmay be transmitted with the processed data to allow more faithfulreproduction. In a pattern recognition context, the error function mayprovide useful data relating to the reliability of a patterncorrelation, or may provide useful data outside of the model andassociated parameters for pattern recognition.

Thus, in the case of an object-extraction model-based processing system,the resulting data stream may be appropriate for both viewing andrecognition. Of course, acoustic data may be likewise processed usingacoustic models with variable parameters. However, in such a system,information for pattern recognition may be filtered, such as eliminatingthe error function or noise data. Further, certain types of objects maybe ignored, for example, under normal circumstances, clouds in the skyprovide little information for pattern recognition and may be removed.In such a system, data intended for viewing or listening will likelycontain all objects in the original data stream, with as much originaldetail as possible given data storage and bandwidth constraints.

An object extraction model based processing system also allows forincreased noise rejection, such as over terrestrial broadcast channels.By transmitting a model, the receiving system may interpolate orextrapolate data to fill in for missing data. By extrapolate, it ismeant that past data is processed to predict a subsequent condition. Byinterpolate, it is meant that data presentation is delayed, and missingdata may therefore be predicted from both past and subsequent datatransmission. Missing portions of images may also be reconstructed fromexisting portions. This reconstruction process is similar to thatdescribed in U.S. Pat. No. 5,247,363, to reconstruct MPEG images; exceptthat where model data is corrupted, the corruption must be identifiedand the corrupt data eliminated and replaced with predicted data.

It is therefore an object according to the present invention to providea programmable control, having a status, responsive to an user input anda signal received from a signal source, comprising a controller, forreceiving the user input and the signal and producing a control output;a memory for storing data relating to an activity of the user; a dataprocessing system for adaptively predicting a most probable intendedaction of the user based on the stored data relating to the activity ofthe user and derived weighing of at least a subset of possible choices,the derivation being based on a history of use, a context of arespective choice and the status of the control; and a user feedbackdata presenting system comprising an output device for presentation of avariable sequence of programming options to the user, including the mostprobable intended action of the user, in a plurality of output messages,the output messages differing in available programming options.

The programmable control may be employed for performing an action basedon user input and an information content of a signal received from asignal source, wherein the output device includes a display device,further comprising a user controlled direct manipulation-type inputdevice, associated with the display device, having a device output, thedevice output being the user input; a plant capable of performing theaction, being responsive to an actuator signal; and the controller,being for receiving data from the device output of the input device andthe signal, and displaying user feedback data on the display device, thelogical sequence of the user feedback data including at least onesequence of options sufficient to define an operable control program,and a presentation of additional programming options if the controlprogram is not operable.

The programmable control may further comprise a user input processingsystem for adaptively determining a viewer preference based on the userinput received by the controller; a program material processing systemfor characterizing the program material based on its content; acorrelator for correlating the characterized content of the programmaterial with the determined viewer preference to produce a correlationindex; and a processor, selectively processing the program materialbased on the correlation index, the data processing system receiving aninput from the processor.

The programmable control may also comprise a plurality of storedprofiles, a processor for characterizing the user input to produce acharacterized user input; and means for comparing the characterized userinput with at least one of the plurality of stored profiles to produce acomparison index, wherein the variable sequence of programming optionsis determined on the basis of the comparison index. The processor forcharacterizing may perform an algorithm on the signal comprising atransform selected from the group consisting of an Affinetransformation, a Fourier transformation, a discrete cosinetransformation and a wavelet transformation.

It is a further object according to the present invention to provide aprogrammable controller for controlling a recording device for recordingan analog signal sequentially on a recording medium having a pluralityof uniquely identifiable storage locations, further comprising asequential recording device for recording the analog signal, and amemory for storing, in a directory location on the recording mediumwhich is separate from the storage location of the analog signal,information relating to the signal, processed to selectively retaincharacterizing information, and an identifier of a storage location onthe recording medium in which the analog signal is recorded.

It is another object according to the present invention to provide acontrol, wherein program material is encrypted, further comprising adecryption system for decrypting the program material if it is selectedto produce unencrypted program material and optionally an associateddecryption event; a memory for storing data relating to the occurrenceof the decryption event; and a central database for storing datarelating to the occurrence of the decryption event in association withdata relating to the viewer.

It is still another object according to the present invention to providea control wherein the user input processing system monitors a pattern ofuser activity and predicts a viewer preference; the program materialprocessing system comprising a processor for preprocessing the programmaterial to produce a reduced data flow information signal substantiallyretaining information relating to the abstract information content ofthe program material and selectively eliminating data not relating tothe abstract information content of the program material and forcharacterizing the information signal based on the abstract informationcontent; and a comparing system for determining if the correlation indexis indicative of a probable high correlation between thecharacterization of the information signal and the viewer preference andcausing the stored program material to be processed by the processingmeans based on the determination. The system according to this aspect ofthe present invention preferably comprises an image program materialstorage and retrieval system.

The present invention further provides a control further comprising amemory for storing a characterization of the program material; an inputfor receiving a feedback signal from the viewer indicating a degree ofagreement with the correlation index determination, wherein the feedbacksignal and the stored characterization are used by the viewer preferencepredicting means to predict a new viewer preference.

According to another aspect of the invention, it is an object to providean image information retrieval apparatus, comprising a memory forstoring compressed data representing a plurality of images; a datastorage system for retrieving compressed data representing at least oneof the plurality of images and having an output; a memory for storingcharacterization data representing a plurality of image types, having anoutput; and an image processor, receiving as inputs the outputs from thedata storage system and the characterization data memory, and producinga signal corresponding to a relation between at least one of theplurality of images of the compressed data and at least one of the imagetypes of the characterization data.

It is a still further aspect of the present invention to provide a videointerface device for a user comprising a data transmission system forsimultaneously transmitting data representing a plurality of programs; aselector for selecting at least one of the plurality of programs, beingresponsive to an input; a program database containing informationrelating to the plurality of programs, having an output; a graphicaluser interface for defining commands, comprising (a) an image displaydevice having at least two dimensions of display, being for providingvisual image feedback; and (b) a multidimensional input device having atleast two dimensions of operability, adapted to correspond to the twodimensions of the display device, and having an output, so that the usermay cause the input device to produce a corresponding change in an imageof the display device by translating an indicator segment of the displayin the at least two dimensions of display, based on the visual feedbackreceived from the display device, the indicator segment being moved to atranslated location of the display device corresponding to a usercommand; and a controller for controlling the graphical user interfaceand for producing the input of the selector, receiving as a control theoutput of the multidimensional input device, the controller receivingthe output of the program database and presenting information relatingto at least one of the plurality of programs on the display deviceassociated with a command, the command being interpreted by the controlmeans as the user command to produce the input of the selector to selectthe at least one of the plurality of programs associated with thecommand.

Another object of the present invention is to provide an apparatus,receiving as an input from a human user having a user characteristic,comprising an input device, producing an input signal from the humanuser input; a display for displaying information relating to the inputfrom the user and feedback on a current state of the apparatus, havingan alterable image type; an input processor for extracting an inputinstruction relating to a desired change in a state of the apparatusfrom the input signal; a detector for detecting one or moretemporal-spatial user characteristics of the input signal, independentof the input instruction, selected from the group consisting of avelocity component, an efficiency of input, an accuracy of input, aninterruption of input and a high frequency component of input; a memoryfor storing data related to the user characteristics; and a controllerfor altering the image type based on the user characteristics. Thecontroller may alter the image type based on an output of the detectorand the stored data so that the display displays an image type whichcorresponds to the detected user characteristics. The controller mayfurther be for controlling the causation of an action on the occurrenceof an event, further comprising a control for receiving the inputinstruction and storing a program instruction associated with the inputinstruction, the control having a memory sufficient for storing programinstructions to perform an action on the occurrence of an event; and amonitor for monitoring an environment of the apparatus to determine theoccurrence of the event, and causing the performance of the action onthe occurrence of the event. The controller may also alter the imagetype based on an output of the detector and the stored data so that thedisplay means displays an image type which corresponds to the detecteduser characteristics.

It is another object of the present invention to provide an adaptiveprogrammable apparatus having a plurality of states, being programmableby a programmer and operating in an environment in which a plurality ofpossible events occur, each of the events being associated withdifferent data, comprising an data input for receiving data; anprogrammer input, producing an input signal from the programmer; amemory for storing data relating to the data input or the input signal;a feedback device for adaptively providing information relating to theinput signal and a current status of the apparatus to the programmer,based on the data input or the programmer input, the stored data, andderived weighing of at least a subset of possible choices, the derivedweighing being based on a history of use, a context of a respectivechoice and the current status of the apparatus; a memory for storingprogramming data associated with the input signal; and a processor,having a control output, for controlling the response of the apparatusrelating to the detection of the input signal or the data in accordancewith the stored programming data, the processor: (a) processing the atleast one of the input signal or the data to reduce an amount ofinformation while substantially retaining an abstract portion of theinformation; (b) storing a quantity of the abstracted information; (c)processing the abstract portion of the information in conjunction withthe stored quantity of abstracted information; and (d) providing thecontrol output based on the processed abstract portion of theinformation and the stored programming data. The apparatus may furthercomprise an input for receiving a programming preference from theprogrammer indicating a plurality of possible desired events; theprocessor further including a correlator for correlating the programmingpreference with the data based on an adaptive algorithm and fordetermining a likelihood of occurrence of at least one of the desiredevents, producing the control output. The apparatus may further comprisean input for receiving feedback from the programmer indicating aconcurrence with the control output of the processor, and modifying theresponse control based on the received feedback to increase a likelihoodof concurrence. The apparatus may still further verify the programmingdata to ensure that the programming data comprise a complete andconsistent set of instructions; and include a feedback system forinteractively modifying the programming data. The apparatus may alsocomprise a chronological database and an accessing system for accessingthe chronological database on the basis of the programming data storedin the memory.

It is also an object according to the present invention to provide anapparatus comprising an input for receiving a programming preferencefrom the programmer indicating a plurality of possible desired events;and a correlator for correlating the programming preference with thedata based on an adaptive algorithm and for determining a likelihood ofoccurrence of at least one of the desired events, producing the output,the output being associated with the initiation of the the response.

The present invention also provides as an object an apparatus comprisingan input for receiving feedback from the programmer indicating aconcurrence with the output of the correlator, and modifying thealgorithm based on the received feedback, the feedback device comprisinga display and the input device is remote from the display, and providinga direct manipulation of display information of the display.

According to an aspect of the present invention, a processor of theprogrammable apparatus verifies the program instructions to ensure thatthe program instructions are valid and executable by the processor; anoutput for providing an option, selectable by the programmer input forchanging an instruction stored by the processor, such that the apparatusenters a state wherein a new instruction may be input to substitute forthe instruction, wherein the processor verifies the instructions suchthat the instructions are valid; and wherein the feedback device furtherpresents information requesting confirmation from the programmer of theinstructions associated with the input signal. The apparatus may furthercomprise a chronological database and an accessing system for accessingthe chronological database on the basis of the program instructionsstored in the memory.

The processor of the programmable apparatus may receive information fromthe input signal and/or from the data input; and may further comprise aninput signal memory for storing at least a portion of the input signalor the data, a profile generator for selectively generating a profile ofthe input signal or the data, and an input signal profile memory forstoring the profile of the input signal or the data separately from theinput signal or the data in the input signal memory. The programmableapparatus may further comprise a processor for comparing the inputsignal or the data with the stored profile of the input signal or thedata to determine the occurrence of an event, and the data optionallycomprises image data and the processor for comparing performs imageanalysis. The image data may comprise data having three associateddimensions obtained by a method selected from the group consisting ofsynthesizing a three dimensional representation based on a machine basedmodel derived from two dimensional image data, synthesizing a threedimensional representation derived from a time series of pixel images,and synthesizing a three dimensional representation based on a imagedata representing a plurality of parallax views each having at least twodimensions.

A user feedback data presenting device according to the presentinvention may comprise a display having a plurality of display images,the display images differing in available programming options.

According to another aspect of the present invention, a program materialprocessing system is provided comprising means for storing templatedata; means for storing the image data; means for generating a pluralityof domains from the stored image data, each of the domains representingdifferent portions of the image information; means for creating, fromthe stored image data, a plurality of addressable mapped rangescorresponding to different subsets of the stored image data, thecreating means including means for executing, for each of the mappedranges, a procedure upon the one of the subsets of the stored image datawhich corresponds to the mapped range; means for assigning identifiersto corresponding ones of the mapped ranges, each of the identifiersspecifying for the corresponding mapped range an address of thecorresponding subset of stored image data; means for selecting, for eachof the domains, the one of the mapped ranges which most closelycorresponds according to predetermined criteria; means for representingat least a portion of the image information as a set of the identifiersof the selected mapped ranges; and means for selecting, from the storedtemplates, a template which most closely corresponds to the set ofidentifiers representing the image information. The means for selectingmay comprise means for selecting, for each domain, the mapped rangewhich is the most similar, by a method selected from at least one of thegroup consisting of selecting a minimum Hausdorff distance from thedomain, selecting the highest cross-correlation with the domain andselecting the lowest mean square error of the difference between themapped range and the domain. The means for selecting may also comprise,for each domain, the mapped range with the minimum modified Hausdorffdistance calculated as D[db,mrb]+D[1−db,1−mrb], where D is a distancecalculated between a pair of sets of data each representative of animage, db is a domain, mrb is a mapped range, 1−db is the inverse of adomain, and 1−mrb is an inverse of a mapped range. The means forrepresenting may further comprise means for determining a feature ofinterest of the image data, selecting a mapped range corresponding tothe feature of interest, storing the identifiers of the selected mappedrange, selecting a further mapped range corresponding to a portion ofimage data having a predetermined relationship to the feature ofinterest and storing the identifiers of the further mapped range.

According to an embodiment of the present invention, the image datacomprises data having three associated dimensions obtained by a methodselected from the group consisting of synthesizing a three dimensionalrepresentation based on a machine based prediction derived from twodimensional image data, synthesizing a three dimensional representationderived from a time series of pixel images, and synthesizing a threedimensional representation based on a image data representing aplurality of parallax views having at least two dimensions.

It is therefore an object of the present invention to provide aprogrammable apparatus for receiving instructions from a programmer andcausing an action to occur on the happening of an event, comprising aninput device, producing an input instruction signal; a control means forreceiving the input instruction signal, and storing a programinstruction associated with the input instruction signal, the controlmeans storing sufficient program instructions to perform an action onthe occurrence of an event, the control means monitoring a status of theapparatus to determine the occurrence of various events, comparing thedetermined events with the program instructions, and performing theaction on the occurrence of the event; a display means for interactivelydisplaying information related to the instructions to be received, andresponsive thereto, controlled by the control means, so that theprogrammer is presented with feedback on a current state of theapparatus and the program instruction; wherein the control means furthercomprises means for detecting one or more characteristics of the inputinstruction signal independent of the program instruction selected fromthe group consisting of a velocity component, an efficiency of input, anaccuracy of input, an interruption of input, a high frequency componentof input and a past history of input by the programmer, whereby when thecontrol means detects a characteristic indicating that the display meansis displaying information in a suboptimal fashion, the control meanscontrols the display means to display information in a more optimalfashion.

It is also an object of the present invention to provide a programmableapparatus for receiving instructions from a programmer and causing anaction to occur on the happening of an event, comprising an inputdevice, producing an input instruction signal; a control means forreceiving the input instruction signal, and storing a programinstruction associated with the input instruction signal, the controlmeans storing sufficient program instructions to perform an action onthe occurrence of an event, the control means monitoring a status of theapparatus to determine the occurrence of various events, comparing thedetermined events with the program instructions, and performing theaction on the occurrence of the event; a display means for interactivelydisplaying information related to the instructions to be received, andresponsive thereto, controlled by the control means, so that theprogrammer is presented with feedback on a current state of theapparatus and the program instruction; wherein the control means furthercomprises means for detecting a need by the programmer for more detailedinformation displayed on the display means, by detecting one or morecharacteristics of the input instruction signal independent of theprogram instruction selected from the group consisting of a velocitycomponent, an efficiency of input, an accuracy of input, an interruptionof input, a high frequency component of input and a past history ofinput by the programmer, whereby when the control means detects acharacteristic indicating that the display means is insufficientlydetailed information, the control means controls the display means todisplay more detailed information.

It is a further object of the present invention to provide aprogrammable apparatus having a data input, the apparatus receivinginstructions from a programmer and causing an action to occur on thereceipt of data indicating an event, comprising an input device,producing an input instruction signal; a control means for receiving theinput instruction signal, and storing a program instruction associatedwith the input instruction signal, the control means storing sufficientprogram instructions to perform an action on the receipt of dataindicating an event, the control means monitoring the data input; adisplay means for interactively displaying information related to theinstructions to be received, and responsive thereto, controlled by thecontrol means, so that the programmer is presented with feedback on acurrent state of the apparatus and the program instruction; wherein thecontrol means receives a programming preference indicating a desiredevent from the input device which does not unambiguously define theevent, and the control means monitors the data and causes the occurrenceof the action when a correlation between the programming preference andthe monitored data is above a predetermined threshold, indicating alikely occurrence of the desired event. It is also object of the presentinvention to provide the programmable aforementioned apparatus, whereinthe input device is remote from the display means, and provides a directmanipulation of display information of the display means, furthercomprising means for verifying the program instructions so that theprogram instructions are executable by the control means. The controlmeans may further comprise a calendar or other chronological database.

Another object of the present invention provides a programmableinformation storage apparatus having a data input, for receiving data tobe stored, the apparatus receiving instructions from a programmer andcausing an action to occur on the receipt of data indicating an event,comprising means for storing data from the data input; an input device,producing an input instruction signal; a control means for receiving theinput instruction signal, and storing a program instruction associatedwith the input instruction signal, the control means storing sufficientprogram instructions to perform an action on the receipt of data fromthe data input indicating an event, the control means monitoring thedata input to determine the occurrence of various events, comparing thedetermined events with the program instructions, and performing forstoring the data the action on the occurrence of the event; wherein thecontrol means receives identifying data from at least one of the inputdevice and the data input, the identifying data being stored separatelyfrom the input data on a storage medium. The programmable informationstorage apparatus may also include means for reading the identifyingdata stored separately on the storage medium, and may also receive as aninput the identifying data.

It is also an object of the present invention to provide a programmableapparatus, wherein the control means provides an option, selectable bythe input means in conjunction with the display means, for changing aninput program instruction prior to execution by the control means, sothat the apparatus enters a state wherein a new program instruction maybe input to substitute for the changed input step, wherein the controlmeans verifies the program instructions so that the program instructionsare executable by the control means.

It is still another object of the present invention to provide aprogrammable apparatus, wherein the control means further causes thedisplay means to display a confirmation screen after the programinstructions are input, so that the programmer may confirm the programinstructions.

Another object of the present invention is to provide a programmableinformation storage apparatus, wherein the control means furthercomprises means for recognizing character data present in a data streamof the input data, the identifying data comprising the recognizedcharacter data.

It is a still further object of the present invention to provide a videotape recording apparatus, comprising a video signal receiving device, arecording device for recording the video signal, wherein the controlanalyzes the video signal for the presence of a symbol, and recognizesthe symbol as one of a group of recognized symbols, and the controlstores the recognized symbol separately from the video signal.

Another object of the present invention is to provide a recording devicefor recording an analog signal sequentially on a recording medium,comprising means for characterizing the analog signal, wherein datarepresenting the characterization and a location of the analog signal onthe recording medium are stored in a directory location on the recordingmedium separately from the analog signal.

It is a further object of the present invention to provide an interfacefor a programmable control for input of a program for a controller toexecute, which performs an action based on an external signal,comprising an input device, a controller for receiving data from theinput device and from an external stimulus, a plant being controlled bythe controller based on an input from the input device and the externalstimulus, and a display device being controlled by the controller, forproviding visual feedback to a user operating the input device, whereina predetermined logical sequence of programming options is presented tothe user on the display device, in a plurality of display screens, eachof the display screens differing in available programming choices; thelogical sequence including a correct sequence of choices to set anoperable control program, so that no necessary steps are omitted; theexternal stimulus comprises a timing device, and the display comprises adisplay option for programming the plant to perform an action at a timewhich is input through the input device as a relative position on thedisplay device, the relative position including a means for displayingan absolute time entry and means for displaying a relative time entry,the display also comprising a display option means for performing anaction at a time; the control comprises means for presenting the user,on the display device, with a most probable action, which may beselected by the user through activation of the input device withoutentering data into the controller through the input device relating toboth the action and the event; the display also comprising means forindicating completion of entry of a programming step, which meansindicates to the user an indication that the programming step is notcompleted if information necessary for execution of the step is notavailable to the controller; and the controller being capable ofcontrolling the display device to present information to the userrelating to the use of the apparatus if necessary for use of the deviceby the user.

Another object of the present invention provides a system for presentinga program to a viewer, comprising a source of program material; meansfor determining a viewer preference, the viewer preference optionallybeing context sensitive; means for receiving the program material fromthe source; means for characterizing the program material based on itscontent; means for correlating the characterized content of the programmaterial with the determined viewer preference to produce a correlationindex; and means for presenting the program material to the viewer, ifthe correlation index indicates a probable high correlation between thecharacterization of the program material and the viewer preference.

Another object of the present invention is to provide a system forpresenting a program to a viewer, comprising a source of programmaterial; means for determining a viewer preference; means for receivingthe program material from the source; means for storing the programmaterial; means for preprocessing the program material to produce areduced data flow information signal retaining information relating to acharacter of the program material and eliminating data not necessary tocharacterize the program material; means for characterizing theinformation signal based on its content; means for correlating thecharacterized content of the information signal with the determinedviewer preference to produce a correlation index; and means forpresenting the stored program material to the viewer, if the correlationindex indicates a probable high correlation between the characterizationof the information signal and the viewer preference. The system may alsoinclude a means for storing the information signal, wherein thecharacterizing means characterizes the stored information signal, andalso a memory for storing the program material while the characterizingmeans produces characterized content and the correlating means producesthe correlation index.

Still another object of the present invention is to provide a system,wherein the program material is encrypted, further comprising means fordecrypting the program material to produce a decryption event; and meansfor charging an account of the viewer based on the occurrence of adecryption event. Thus, a decryption processor and an accountingdatabase are provided for these purposes.

Another object of the present invention is to allow the means forcharacterizing the program material to operate without causing adecryption event. Thus, the data stream may include characterizationdata specifically suitable for processing by a characterizing system, orthe decryption processor may be provided with multiple levels offunctionality, or both. Further, the system may comprise a memory forstoring the program material while the characterizing means producescharacterized content and the correlating means produces the correlationindex. The characterizing means may also characterize the programmaterial stored in memory, and the program material stored in memory maybe compressed.

Another object of the present invention is to provide a controller forcontrolling a plant, having a sensor for sensing an external event andproducing a sensor signal, an actuator, responsive to an actuatorsignal, for influencing the external event, and a control means forreceiving the sensor signal and producing an actuator signal, comprisingmeans for inputting a program; means for storing the program; means forcharacterizing the sensor signal to produce a characterized signal; andmeans for comparing the characterized signal with a pattern stored in amemory to produce a comparison index, wherein the actuator signal isproduced on the basis of the comparison index and the program, whereinthe characterization comprises an Affine transformation of the sensorsignal. The characterization may comprise one or more transformationselected from the group consisting of an Affine transformation, aFourier transformation, a Gabor transformation, and a wavelettransformation.

It is another object of the present invention to provide a method forautomatically recognizing digital image data consisting of imageinformation, the method comprising the steps performed by a dataprocessor of storing a plurality of templates; storing the image data inthe data processor; generating a plurality of addressable domains fromthe stored image data, each of the domains representing a portion of theimage information; creating, from the stored image data, a plurality ofaddressable mapped ranges corresponding to different subsets of thestored image data, the creating step including the substep of (a)executing, for each of the mapped ranges, a corresponding procedure uponthe one of the subsets of the stored image data which corresponds to themapped ranges; (b) assigning identifiers to corresponding ones of themapped ranges, each of the identifiers specifying for the correspondingmapped range a procedure and a address of the corresponding subset ofthe stored image data; (c) optionally subjecting a domain to a transformselected from the group consisting of a predetermined rotation, aninversion, a predetermined scaling, and a predetermined preprocessing inthe time, frequency, and/or wavelet domain; (d) selecting, for each ofthe domains or transformed domains, the one of the mapped ranges whichmost closely corresponds according to predetermined criteria; (e)representing the image information as a set of the identifiers of theselected mapped ranges; and (f) selecting, from the stored templates, atemplate which most closely corresponds to the set of identifiersrepresenting the image information. The step of selecting the mappedranges may also include the substep of selecting, for each domain, amost closely corresponding one of the mapped ranges.

It is another object of the present invention to provide a methodwherein the step of selecting the most closely corresponding one of themapped ranges includes the step of selecting, for each domain, themapped range which is the most similar, by a method selected from one ormore of the group consisting of selecting minimum Hausdorff distancefrom the domain, selecting the highest cross-correlation with thedomain, selecting the highest fuzzy correlation with the domain andselecting the minimum mean square error with the domain.

Another object of the present invention provides a method wherein thestep of selecting the most closely corresponding one of mapped rangesincludes the step of selecting, for each domain, the mapped range withthe minimum modified Hausdorff distance calculated asD[db,mrb]+D[1−db,1−mrb], where D is a distance calculated between a pairof sets of data each representative of an image, db is a domain, mrb isa mapped range, 1−db is the inverse of a domain, and 1−mrb is an inverseof a mapped range.

Another object of the present invention provides a method wherein thedigital image data consists of a plurality of pixels each having one ofa plurality of associated color map values, further comprising the stepsof optionally transforming the color map values of the pixels of eachdomain by a function including at least one scaling function for eachaxis of the color map, each of which may be the same or different, andselected to maximize the correspondence between the domains and rangesto which they are to be matched; selecting, for each of the domains, theone of the mapped ranges having color map pixel values which mostclosely correspond to the color map pixel values of the domain accordingto a predetermined criteria, wherein the step of representing the imagecolor map information includes the substep of representing the imagecolor map information as a set of values each including an identifier ofthe selected mapped range and the scaling functions; and selecting amost closely corresponding stored template, based on the identifier ofthe color map mapped range, the scaling functions and the set ofidentifiers representing the image information. The first criteria maycomprise minimizing the Hausdorff distance between each domain and theselected range.

Another object of the present invention is to provide a method furthercomprising the steps of storing delayed image data, which represents animage of a moving object differing in time from the image data in thedata processor; generating a plurality of addressable further domainsfrom the stored delayed image data, each of the further domainsrepresenting a portion of the delayed image information, andcorresponding to a domain; creating, from the stored delayed image data,a plurality of addressable mapped ranges corresponding to differentsubsets of the stored delayed image data; matching the further domainand the domain by subjecting a further domain to one or both of acorresponding transform selected from the group consisting of a nulltransform, a rotation, an inversion, a scaling, a translation and afrequency domain preprocessing, which corresponds to a transform appliedto a corresponding domain, and a noncorresponding transform selectedfrom the group consisting of a rotation, an inversion, a scaling, atranslation and a frequency domain preprocessing, which does notcorrespond to a transform applied to a corresponding domain; computing amotion vector between one of the domain and the further domain, or theset of identifiers representing the image information and the set ofidentifiers representing the delayed image information, and storing themotion vector; compensating the further domain with the motion vectorand computing a difference between the compensated further domain andthe domain; selecting, for each of the delayed domains, the one of themapped ranges which most closely corresponds according to predeterminedcriteria; representing the difference between the compensated furtherdomain and the domain as a set of difference identifiers of a set ofselected mapping ranges and an associated motion vector and representingthe further domain as a set of identifiers of the selected mappingranges; determining a complexity of the difference based on a density ofrepresentation; and when the difference has a complexity below apredetermined threshold, selecting, from the stored templates, atemplate which most closely corresponds to the set of identifiers of theimage data and the set of identifiers of the delayed image data.

Another object of the present invention provides an apparatus forautomatically recognizing digital image data consisting of imageinformation, comprising means for storing template data; means forstoring the image data; means for generating a plurality of addressabledomains from the stored image data, each of the domains representing adifferent portion of the image information; means for creating, from thestored image data, a plurality of addressable mapped rangescorresponding to different subsets of the stored image data, thecreating means including means for executing, for each of the mappedranges, a procedure upon the one of the subsets of the stored image datawhich corresponds to the mapped range; means for assigning identifiersto corresponding ones of the mapped ranges, each of the identifiersspecifying for the corresponding mapped range an address of thecorresponding subset of stored image data; means for selecting, for eachof the domains, the one of the mapped ranges which most closelycorresponds according to predetermined criteria; means for representingthe image information as a set of the identifiers of the selected mappedranges; and means for selecting, from the stored templates, a templatewhich most closely corresponds to the set of identifiers representingthe image information.

It is also an object of the present invention to provide a method andsystem for processing broadcast material having a first portion and asecond portion, wherein the first portion comprises an content segmentand the second portion comprises a commercial segment, in order to allowalteration in the presentation of commercial segments, based on therecipient, commercial sponsor, and content provider, while providingmeans for accounting for the entire broadcast.

Another object of an embodiment of the present invention provides anapparatus comprising a user interface, receiving a control input and auser attribute from the user; a memory system, storing the control inputand user attribute; an input for receiving content data; means forstoring data describing elements of the content data; means forpresenting information to the user relating to the content data, theinformation being for assisting the user in defining a control input,the information being based on the stored user attribute and the datadescribing elements of the content data; and means for processingelements of the content data in dependence on the control input, havingan output. This apparatus according to this embodiment may be furtherdefined as a terminal used by users of a television program deliverysystem for suggesting programs to users, wherein the user interfacecomprises means for gathering the user specific data to be used inselecting programs; the memory system comprises means, connected to thegathering means, for storing the user specific data; the input forreceiving data describing elements of the content data comprises meansfor receiving the program control information containing the programdescription data; and the processing means comprises program selectionmeans, operably connected to the storing means and the receiving means,for selecting one or more programs using a user's programmingpreferences and the program control information. In this case, theprogram selection means may comprise a processor, wherein the userprogramming preferences are generated from the user specific data; andmeans, operably connected to the program selection means, for suggestingthe selected programs to the user. The apparatus processing meansselectively may records the content data based on the output of theprocessing means. Further, the presenting means presents information tothe user in a menu format. The presenting means may comprises means formatching the user attribute to content data.

The data describing elements of an associated data stream may, forexample, comprise a program guide generated remotely from the apparatusand transmitted in electronically accessible form; data defined by ahuman input, and/or data defined by an automated analysis of the contentdata.

Acording to another embodiment, the present invention comprises amethod, comprising the steps of receiving data describing an userattribute; receiving a content data stream, and extracting from thecontent data stream information describing a plurality of programoptions; and processing the data describing a user attribute and theinformation describing a plurality of program options to determine alikely user preference; selectively processing a program option based onthe likely user preference. The method may be embodied in a terminal fora television program delivery system for suggesting programs to usersfor display on a television using program control information and userspecific data. In that case, the step of receiving data describing anuser attribute may comprise gathering user specific data to be used inselecting programs, and storing the gathered user specific data; thestep of receiving a content data stream, may comprise receiving bothprograms and program control information for selecting programs as theinformation describing a plurality of program options; the selectivelyprocessing step may comprise selecting one or more programs using auser's programming preferences and the received program controlinformation, wherein the user programming preferences are generated fromthe user specific data; and the method further including the step ofpresenting the program or information describing a program option forthe selected programs to the user.

The user attribute may comprise a semantic description of a preference,or some other type of description, for example a personal profile, amood, a genre, an image represnting or relating to a scene, ademographic profile, a past history of use by the user, a preferenceagainst certain types of media, or the like. In the case of a semanticpreference, the data processing step may comprise determining a semanticrelationship of the user preference to the information describing aplurality of program options. The program options may, for example, betransmitted as an electronic program guide, the information beingin-band with the content (being transmitted on the same channel), on aseparate channel or otherwise out of band, through a separatecommunications network, e.g., the Internet, dial-up network, or otherstreaming or packet based communications system, or by physical transferof a computer-readable storage medium, such as a CD-ROM or floppy disk.The electronic program guide may include not only semantic orhuman-readable information, but also other types of metadata relating toor describing the program content.

In a further embodiment of the present invention, it is an object toprovide a device for identifying a program in response to userpreference data and program control information concerning availableprograms, comprising means for gathering the user preference data;means, connected to the gathering means, for storing the gathered userpreference data; means for accessing the program control information;and means, connected to the storing means and accessing means, foridentifying one or more programs based on a correspondence between auser's programming preferences and the program control information. Forexample, the identifying means identifies a plurality of programs, asequence of identifications transmitted to the user being based on adegree of correspondence between a user's programming preferences andthe respective program control information of the identified program.The device my selectively record or display the program, or identify theprogram for the user, who may then define the appropriate action by thedevice. Therefore, a user may, instead of defining “like” preferences,may define “dislike” preference, which are then used to avoid or filtercertain content. Thus, this feature may be used for censoring orparental screening, or merely to avoid unwanted content. Thus, thedevice comprises a user interface adapted to allow interaction betweenthe user and the device for response to one or more of the identifiedprograms. The device also preferably comprises means for gathering theuser specific data comprises means for monitoring a response of the userto identified programs.

It is a further object of the invention to provide a device which servesas a set top terminal used by users of a television program deliverysystem for suggesting programs to users using program controlinformation containing scheduled program description data, wherein themeans for gathering the user preference data comprising means forgathering program watched data; the means, connected to the gatheringmeans, for storing the gathered user preference data comprising means,connected to the gathering means, for storing the program watched data;the means for accessing the program control information comprising meansfor receiving the program control information comprising the scheduledprogram description data; the means, connected to the storing means andaccessing means, for identifying one or more programs based on acorrespondence between a user's programming preferences and the programcontrol information, being for selecting at least one program forsuggestion to the viewer, comprising: means for transforming the programwatched data into preferred program indicators, wherein a programindicator comprises a program category with each program category havinga weighted value; means for comparing the preferred program indicatorswith the scheduled program description data, wherein each scheduledprogram is assigned a weighted value based on at least one associatedprogram category; means for prioritizing the scheduled programs fromhighest weighted value programs to lowest weighted value programs; meansfor indicating one or more programs meeting a predetermined weightthreshold, wherein all other programs are excluded from programsuggestion; and means, operably connected to the program selectionmeans, for displaying for suggestion the selected programs to the user.

It is a further aspect of the invention to provide device a devicecomprising: a data selector, for selecting a program from a data stream;an encoder, for encoding programs in a digitally compressed format; amass storage system, for storing and retrieving encoded programs; adecoder, for decompressing the retrieved encoded programs; and anoutput, for outputting the decompressed programs.

Therefore, the present invention provides a system and method for makinguse of the available broadcast media forms for improving an efficiencyof matching commercial information to the desires and interests of arecipient, improving a cost effectiveness for advertisers, improving aperceived quality of commercial information received by recipients andincreasing profits and reducing required information transmittal bypublishers and media distribution entities.

This improved advertising efficiency is accomplished by providing asystem for collating a constant or underlying published content workwith a varying, demographically or otherwise optimized commercialinformation content. This commercial information content therefore neednot be predetermined or even known to the publisher of the underlyingworks, and in fact may be determined on an individual receiver basis. Itis also possible to integrate the demographically optimized informationwithin the content. For example, overlays in traditional media, andelectronic substitutions or edits in new media, may allow seamlessintegration. The content alteration need not be only based on commercialinformation, and therefore the content may vary based on the user orrecipient.

The technologies emphasize adaptive pattern recognition of both the userinput and data, with possible use of advanced signal processing andneural networks. These systems may be shared between the interface andoperational systems, and therefore a controller for a complex system maymake use of the intrinsic processing power available, rather thanrequiring additional computing resources, although this unification isnot required. In fact, while hardware efficiency dictates that near termcommercial embodiments employ common hardware for the interface systemand the operational system, future designs may successfully separate theinterface system from the operational system, allowing portability andefficient application of a single interface system for a number ofoperational systems.

The adaptive nature of the technologies derive from an understandingthat people learn most efficiently through the interactive experiencesof doing, thinking, and knowing. Users change in both efficiency andstrategy over time. To promote ease-of-use, efficiency, and lack offrustration of the user, the interface of the device is intuitive andself explanatory, providing perceptual feedback to assist the operatorin communicating with the interface, which in turn allows theoperational system to identify of a desired operation. Another importantaspect of man-machine interaction is that there is a learning curve,which dictates that devices which are especially easy to master becomefrustratingly elemental after continued use, while devices which havecomplex functionality with many options are difficult to master and maybe initially rejected, or used only at the simplest levels. The presenttechnologies address these issues by determining the most likelyinstructions of the operator, and presenting these as easily availablechoices, by analyzing the past history data and by detecting the“sophistication” of the user in performing a function, based on allinformation available to it. The context of use is also a factor in manysystems. The interface seeks to optimize the interface adaptively andimmediately in order to balance and optimize both quantitative andqualitative factors. This functionality may greatly enhance the qualityof interaction between man and machine, allowing a higher degree ofoverall system sophistication to be tolerated.

The interface system analyzes data from the user, which may be both theselections made by the user in context, as well as the efficiency bywhich the user achieves the selection. Thus, information concerning boththe endpoints and path are considered and analyzed by the human userinterface system.

The interface may be advantageously applied to an operational systemwhich has a plurality of functions, certain of which are unnecessary orare rarely used in various contexts, while others are used with greaterfrequency. In such systems, the application of functionality may bepredictable. Therefore, the present technologies provide an optimizedinterface system which, upon recognizing a context, dynamicallyreconfigures the availability or ease of availability of functions andallows various functional subsets to be used through “shortcuts”. Theinterface presentation will therefore vary over time, use and theparticular user.

The advantages to be gained by using an intelligent data analysisinterface for facilitating user control and operation of the system aremore than merely reducing the average number of selections or time toaccess a given function. Rather, advantages also accrue from providing ameans for access and availability of functions not necessarilypreviously existing or known to the user, improving the capabilities andperceived quality of the product.

Further improvements over prior interfaces are also possible due to theavailability of pattern recognition functionality as a part of theinterface system. In those cases where the pattern recognition functionsare applied to large amounts of data or complex data sets, in order toprovide a sufficient advantage and acceptable response time, powerfulcomputational resources, such as powerful RISC processors, advanced DSPsor neural network processors are made available to the interface system.On the other hand, where the data is simple or of limited scope, aspectsof the technology may be easily implemented as added software-basedfunctionality in existing products having limited computationalresources.

The application of these technologies to multimedia data processingsystems provides a new model for performing image pattern recognitionand for the programming of applications including such data. The abilityof the interface to perform abstractions and make decisions regarding acloseness of presented data to selection criteria makes the interfacesuitable for use in a programmable control, i.e., determining theexistence of certain conditions and taking certain actions on theoccurrence of detected events. Such advanced technologies might beespecially valuable for disabled users.

In a multimedia environment, it may be desirable for a user to performan operation on a multimedia data event. Past systems have requiredexplicit indexing or identification of images and events. The presenttechnologies, however, allow an image, diagrammatic, abstract orlinguistic description of the desired event to be acquired by theinterface system from the user and applied to identify or predict themultimedia event(s) desired, without requiring a separate manualindexing or classification effort. These technologies may also beapplied to single media data.

The interface system analyzes data from many different sources for itsoperation. Data may be stored or present in a dynamic data stream. Thus,in a multimedia system, there may be a real-time video feed, a storedevent database, as well as an exemplar or model database. Further, sincethe device is adaptive, information relating to past experience of theinterface, both with respect to exposure to data streams and userinteraction, is also stored.

This data analysis aspect of the interface system may be substantiallyprocessor intensive, especially where the data includes abstract orlinguistic concepts or images to be analyzed. Interfaces which do notrelate to the processing of such data may be implemented with simplerhardware. On the other hand, systems which handle complex data types maynecessarily include sophisticated processors, adaptable for use by theinterface system. A portion of the data analysis may also overlap thefunctional analysis of the data for the operational system.

Other objects and features of the present invention will become apparentfrom the following detailed description considered in conjunction withthe accompanying drawings. It is to be understood, however, that thedrawings are designed solely for the purposes of illustration and not asa definition of the limits of the invention, for which reference shouldbe made to the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present invention are shown in the figures in thedrawings, in which:

FIG. 1 is a flow chart of the steps required to set a VCR;

FIG. 2 shows a graphical comparison of required and extra keypresses forthe prior art and the interface of the present invention;

FIG. 3 graphically shows the differences in seconds between total timefor the prior art for each user;

FIG. 4 graphically shows the differences in seconds between total timefor the interface of the present invention for each user;

FIG. 5 graphically shows the programming steps for the comparison of theprior art and the interface of the present invention;

FIG. 6 graphically shows comparative statistics by user comparing theprior art and the interface of the present invention;

FIGS. 7 and 8 graphically show the critical steps in programming theprior art and the interface of the present invention;

FIG. 9 graphically shows the number of keypresses made by testparticipants comparing the prior art and the interface of the presentinvention;

FIG. 10 graphically shows the comparison of the actual and theoreticalnumber of keypresses necessary for programming the prior art and theinterface of the present invention;

FIG. 11 graphically compares the actual and theoretical time necessaryfor programming the prior art and the interface of the presentinvention;

FIGS. 12 a and 12 b graphically compares the actual and theoretical timenecessary for setting the programs in the prior art and the interface ofthe present invention;

FIGS. 13 and 14 graphically show the percentage time for the criticalsteps in programming the prior art and the interface of the presentinvention;

FIG. 15 is a flow diagram of a predictive user interface of the presentinvention;

FIG. 16 is a flow diagram of the program input verification system ofthe present invention;

FIG. 17 is a flow diagram of a predictive user preference awareinterface of the present invention;

FIG. 18 is a block diagram of a non-program information featureextraction circuit of the present invention;

FIG. 19 is a diagram of a block of information for a catalog entry ofthe present invention;

FIG. 20 is a block diagram of a digital information and analog signalreading/recording apparatus;

FIG. 21 is a block diagram of a user level determining system of thepresent invention;

FIG. 22 is a block diagram of a template-based pattern recognitionsystem of the present invention;

FIG. 23 is a block diagram of a control system of the present inventionincorporating a pattern recognition element and an interface;

FIG. 24 is a block diagram of a control system for characterizing andcorrelating a signal pattern with a stored user preference of thepresent invention;

FIG. 25 is a block diagram of a multiple video signal input apparatus,with pattern recognition, data compression, data encryption, and a userinterface of the present invention;

FIG. 26 is a block diagram of a control system for matching a templatewith a sensor input, of the present invention;

FIGS. 27, 28 and 29 are flow diagrams of an iterated function systemmethod for recognizing a pattern according to the present invention;

FIG. 30 is a semi-cartoon flow diagram of the object decomposition andrecognition method of the present invention;

FIG. 31 is a block diagram of an adaptive interface system according tothe present inventions

FIG. 32 shows a clock diagram of a system in accordance with the presentinvention; and

FIG. 33 shows a flow chart in accordance with an embodiment of thepresent invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The preferred embodiments of the present invention will now be describedwith reference to the Figures. Identical elements in the various figuresare designated with the same reference numerals.

EXAMPLE 1

VCR Interface

A preferred embodiment of the interface of the present invention,described in the present example, provides automatic sequencing ofsteps, leading the user through the correct sequence of actions to set aprogram on the screen, so that no necessary steps are omitted, and nooptional steps are accidentally or unintentionally omitted. These stepsare shown diagrammatically in FIG. 15 of the present invention. Inaddition, such a system does not burden the user with the necessity ofinputting superfluous information, nor overwhelm the user with thedisplay of unnecessary data. See, Hoffberg, Linda I., “AN IMPROVED HUMANFACTORED INTERFACE FOR PROGRAMMABLE DEVICES: A CASE STUDY OF THE VCR”,Master's Thesis, Tufts University; Hoffberg, Linda I., “Designing UserInterface Guidelines For Time-Shift Programming of a Video CassetteRecorder (VCR)”, Proc. of the Human Factors Soc. 35th Ann. Mtg. pp.501-504 (1991); and Hoffberg, Linda I., “Designing a ProgrammableInterface for a Video Cassette Recorder (VCR) to Meet a User's Needs”,Interface 91 pp. 346-351 (1991). See also, U.S. patent application Ser.No. 07/812,805, incorporated herein by reference in its entirety,including appendices and incorporated references.

Many design considerations were found to be important in the improvedinterface of the present invention:

The interface should preferably employ only minimal amounts ofabbreviations and the use of complete words is especially preferred,except where a standard abbreviation is available or where an “iconic”or symbolic figure or textual cue is appropriate. Thus, standardabbreviations and symbols are acceptable, and displayed characterstrings may be shortened or truncated in order to reduce the amount ofinformation that is to be displayed, where necessary or desirable. Anoption may be provided to the user to allow full words, which maydecrease the information which may be conveyed on each screen andincrease the number of screens that must be displayed, or abbreviationsand symbols, which may minimize the number of displayed screens ofinformation, thus allowing the user to make the compromise. This aspectof the system may also be linked to the adaptive user level function ofthe present invention, wherein abstract symbols and abbreviations arepresented to advanced users, while novices are presented with fullwords, based on an implicit indication of user level. These abstractsymbols and abbreviations may be standard elements of the system, oruser designated icons. Of course, the user could explicitly indicate hispreference for the display type, thus deactivating the automaticadaptive user level function.

If multiple users use the device, then the device identifies therelevant users. This may be by explicit identification by keyboard, barcode, magnetic code, smart card (which may advantageously include a userprofile for use with a number of devices), an RF-ID or IR-IDtransponder, voice recognition, image recognition, or fingerprintidentification. It is noted that smart cards or other intelligent ordata-containing identifications systems may be used with different typesof devices, for example video, audio, home appliances, HVAC andautomobile systems.

Where a new user is identified to the system, an initial query may bemade to determine an optimum initial user level. This allows furtheridentification of the user and preference determination to occur moreefficiently.

In applications in which a user must program an event on a certain date,at a certain time, a built-in calendar menu screen is preferablyemployed so that the user cannot set the device with a program step thatrelies on a non-existent date. Technology that will help eliminate thehuman problem of setting the wrong (yet existing) date may also beemployed. Such technology might include accessing an on-line or othertype of database containing media programming information, and promptingthe user regarding the selected choice. In situations where it isapplicable, the interface should indicate to the user the number ofcharacters the interface is expecting, such as when entering the year.

The interface system provides an easily accessible CHANGE, CANCEL orUNDO (single or multiple level) feature, which facilitates backtrackingor reprogramming the immediately previously entered information ratherthan forcing the user to repeat all or a substantial portion of theprogramming steps. A method of the type described is shown in FIG. 16 ofthe present invention. User input is also facilitated by the provisionof frequently used settings as explicit choices, such as, referring tothe VCR example, “Record today,” “Record tomorrow,” “Noon,” and“Midnight,” so that the user does not have to specify a date in thesecases. This will eliminate extra keypresses, and reduce the programmingtime. In addition, this could eliminate user errors. Frequently usedchoices for program selections are also provided to the user to reducethe number of programming steps necessary and provide the user with allthe frequently used selections. The especially preferred choices are“Once On .”, “Once a Week on .”, “Monday-Friday at .”, “Everyday at .”.These redundant, complex instructions reduce the number of keystrokesrequired for data entry, and reduce the amount of programming timerequired.

The presently described interface system also provides, in the eventthat a color screen is available, conservatively used color coding,which allows the user to effectively and quickly acknowledge thefunction of each aspect of the screen. When programming, the preferredcolors are royal blue for “help,” red for mistakes, light blue forinformation previously entered, and yellow for current information beingentered. Of course, other colors could be used, according to the user'sor designer's preference, cultural differences, and display parameters.

When viewing, it is preferable that screen colors change to indicatestatus changes, such as viewed/unviewed, or to categorize the shows.

The interface includes a confirmation screen which displays to the userall of the categories and selections previously explicitly entered orotherwise inferred, and should be easily understandable. This is shownin FIG. 15 of the present invention. All of the necessary information isdisplayed on this screen, in addition to the change and cancel options,if possible.

The entering of information on each screen is preferably consistentthroughout the various interface options and levels. All of the screenspreferably have similar layouts. “Buttons” or screen locations which arekeyed to a particular function, which appear on multiple screens, shouldappear in approximately the same location on all screens. However, incertain cases, relatively more important information on a given screenmay be displayed more prominently, and possibly in a different screenlocation, in order to reduce the search time. Further, when otherfactors dictate, each screen may be independently optimized for theprescribed function. For example, a representation of an analog clockdial may be used to set time information. However, even if the formatdoes change, a standard scheme should be maintained, such as the use ofa particular color to indicate that a particular program aspect has beenchanged.

The interface should display data consistent with standards andconventions familiar to users. For, e.g., when entering dates, users aremost familiar with calendars. However, this type of presentation ofchoices does not eliminate the human problem of entering incorrectinformation, e.g., setting a wrong, but existing, date. The problem ofensuring the accuracy of user input may be addressed by an intelligentinterface which stores data concerning programming, user preferences,and by means of some logical method, such as Boolean logic, fuzzy logic,neural network theory, or any other system which may be used to generatea prediction, to determine if an entry is likely in error, by comparingthe prediction with the entry. Of course, these predictive systems wouldalso provide an initial default entry, so that an a priori most probablyaction or actions may be initially presented to the user.

In addition to following conventions of information presentation to theuser, the interface of the present invention may also provide emulationsof other user interfaces of which a particular user may be familiar,even if these are not optimized according to the presently preferredembodiments of the present invention, or not otherwise well known. Theseemulations need not even be of the same type of device, so that a broadbased standard for entry of information into a programmable controls,regardless of their type, may be implemented. By allowing emulation, theinterface could provide compatibility with a standard or proprietaryinterface, with enhanced functionality provided by the features of thepresent interface.

These enhanced functional intelligent aspects of the controller may beimplemented by means of software programming of a simple microcomputer,or by use of more specialized processors, such as a Fuzzy Set Processor(FSP) or Neural Network Processor to provide real-time responsiveness,eliminating delays associated with the implementation of complexcalculations on general purpose computing devices.

In the various embodiments according to the present invention, variouscontrol strategies are employed. Depending on the application, fuzzy setprocessors (FSP's) may be preferred because they have the advantage ofbeing easier to program through the use of presumptions or rules formaking the fuzzy inferences, which may be derived by trial and error orthe knowledge of experts, while Neural Networks are less easilyexplicitly programmed and their network weighing values are not easilyunderstood in the abstract, but these systems may be applied to learnappropriate responses from test data. Thus, neural networks tend torequire extensive “training”, while Fuzzy Set Processors may beexplicitly programmed without the need of duplicating or simulatingactual operating conditions, but may require “fine tuning”.

The most frequently used choices preferably should be displayed as thedefault setting. The screen cursor preferably appears at the “accept”screen button, when the screen is displayed. This default can either beset in advance, or acquired by the system. In the case of acquireddefaults, these may be explicitly set by the user or adaptively acquiredby the system through use. The interface of the present invention may betaught, in a “teach” mode, the preferences of the user, or may alsoacquire this information by analyzing the actual choices made by theuser during operation of the interface and associated controller. Thistype of operation is shown schematically in FIG. 15 of the presentinvention. The options of “Midnight” (12:00 AM) and “Noon” (12:00 PM)should preferably be present, as some people often become confused whendistinguishing between them. Icons, such as those indicative of the“sun” and the “moon”, may also be used to facilitate data entry for AMand PM. The interface should preferably utilize an internal clock andcalendar so that the user cannot set the time or program to record on anonexistent date. Such a system could also compensate fordaylight-savings time seasonal adjustments.

The cursor is preferably distinctive and readily distinguished fromother parts of the screen. This may be by color, attribute (i.e.blinking), size, font change of underlying text, or by other means.

The user can preferably exit the programming sequence at any time byselecting a “Main Menu” button which may exist on the lower left-handcorner of every screen. The user is preferably provided with an adequateamount of feedback, and error messages should be directive in nature.Some form of an acknowledgement is preferably displayed after eachentry. The user should preferably not be able to go to the nextprogramming step until the current step has been completed. A message toconvey why the user can not continue should appear when an attempt toprematurely continue is recognized.

The “help” function is available for when the user does not know what todo. The “help” screen(s) preferably explains the functions of each ofthe available buttons or functions, but may also be limited to thosethat are ambiguous. The “help” screen may also be used to indicate acurrent status of the interface and the controller. Further, the “help”function may also provide access to various other functions, such asadvanced options and configurations, and thus need not be limited tomerely providing information on the display. The help system mayincorporate a hypertext-type system, wherein text or informationrelating to concepts that are conceptually linked may be easily accessedby indicating to the interface system that the related information isdesired. To eliminate the possibility of the user trying to makeselections on merely informative help screens, the cursor, in thesecases, should be locked to a choice which returns the user to where theyleft off in the programming sequence, and this choice should behighlighted.

The “help” function may also comprise “balloon help” similar to thesystem adopted by Apple Computer, Inc. in Macintosh Operating System,e.g., 7.0, 7.1, 7.5, etc.

The interface preferably initiates the programming sequence where theuser wants to be, so that the interface has so-called “smart screens”.For example, when a VCR is first powered up or after an extended powerfailure, and the time and date are not stored in the machine, the “setdate” and “set time” screens should appear. The sequence of screens mayalso vary depending on the system predicted requirements of the user andvarious aspects of the improved interface of the present invention. Thisis shown schematically in FIG. 17 of the present invention.

The preferable input device for the interface of the present inventionprovides as few buttons as possible to achieve the requiredfunctionality, thus reducing potential user intimidation, focusing theuser's attention on the interactive display screen, where the availablechoices are minimized to that number necessary to efficiently allow theuser to program the discrete task presented. Such a minimization ofdiscrete inputs facilitates a voice recognition input, which may be usedas an alternative to mechanical input devices. The preferred embodimentincludes a direct-manipulation type interface, in which a physical actof the user causes a proportionate change in the associated interfacecharacteristic, such as cursor position. A computer mouse, e.g. a twodimensional input device, with 1 to 3 buttons is the preferred inputdevice, for use with a general purpose computer as a controller, while atrackball on a remote control device is especially preferred for limitedpurpose controllers because they do not require a flat surface foroperation. Other stationary or movement sensitive input devices may, ofcourse be used, such as joysticks, gyroscopes, sonic echo-location,magnetic or electrostatic location devices, RF phase location devices,Hallpots (joystick-like device with magnets that move with respect toHall effect transducers), etc. The present interface minimizes thenumber of necessary keys present on an input device, while maintainingthe functionality of the interface. It is noted that a strictminimization without consideration of functionality, might lead toinefficiency. For example, in a VCR device, if the user wants to recorda program which airs Monday through Friday, he would have to set fiveseparate programs, rather than one program if a “weeknights” choice ismade available.

The interface preferably should be easy to learn and should not requirethat a user have prior knowledge of the interface in order to use it. Anattempt has been made to minimize the learning curve, i.e., to minimizethe time it takes to learn how to use the device.

Menu options are preferably displayed in logical order or in theirexpected frequencies. Research has shown that a menu-driven interface isbest for applications involving new users and does not substantiallyhinder experienced users. Menu selection is preferably used for taskswhich involve limited choices. They are most helpful for users withlittle or no training. Each menu should preferably allow only oneselection at a time. Most of the information is preferably entered usinga numeric keypad (entry method), rather than using up and down arrowkeys (selection method). In addition, no leading zeros are required forentry. If there is more than one keystroke required, the user must thenselect an “OK” button to continue in the programming sequence. However,if the selection method is used, all of the choices are displayed on thescreen at once. The number of steps required to complete the taskthrough a sequence of menus should be minimized. The choice of wordsused to convey information should not be device specific, i.e., computerterms, but rather normal, everyday terms which are easy to understand.In addition, very few abbreviations should be used. All necessaryinformation which the user needs should preferably be displayed at once.A user preferably should not have to rely on his memory or his previousexperience, in order to find the correct choice, at least at the loweruser levels. If all selections cannot be displayed at once, ahierarchical sequence is preferably used. A main menu should preferablyprovide a top level to which the user can always return and start over.

Searching and learning times should be kept to a minimum in order toobtain a subjectively better interface. The system's logic shouldreflect the users' expectations, offer visual clues and feedback, andstay within human memory limits. For example, the VCR should turn on notonly with the “Power” button, but also when inserting a tape into thedevice. In addition, the sequence of steps for setting the machine torecord, if the user does not indicate implicitly or explicitly that heknows how to use the device, should assume that the user is a novice,and fully prompt the user for elemental items of information. Nothingshould be taken for granted. By developing an improved interface, anattempt is made to: reduce the searching time; reduce the learning time;simplify the entering of data; and, reduce the intimidation experiencedby certain persons when using electronic devices.

Tests by an inventor hereof show that people do not program their VCRsoften, and they often forget the sequence of steps between recordingsessions. Thus, the present invention preferably incorporates anadaptive user level interface, wherein a novice user is presented with asimpler interface with fewer advanced features initially available, sothat there is reduced searching for the basic functions. A more advanceduser is presented with more advanced choices and functions availableinitially, as compared to a novice user.

Thus, as shown in FIG. 17, the user identifies himself to the controllerin block 1701. The controller 1806 of FIG. 18 thereafter uses a storedprofile of the identified user in controlling the interaction with theuser, as shown in block 1702 of FIG. 17, from information stored in thedatabase 1807 of FIG. 18 of the present invention. It has been foundthat in the case of novice users, a greater number of simpleinstructions may be more quickly and easily input rather than apotentially fewer number of a larger set of more complex instructions.It has further been found that, even if presented with a set ofinstructions which will allow a program to be entered with a fewernumber of inputs, a novice user may choose to input the program usingthe simple instructions exclusively, thus employing an increased numberof instructions and being delayed by an increased search time for thoseinstructions that are used, from the larger set.

Other characteristics of this interface include color coding to helpprompt the user as to which data must be entered. Red text signifiesinstructions or errors, yellow text represents data which must beentered or has not been changed, and blue text shows newly enteredprogram data or status information. Blue buttons represent buttons whichshould normally be pressed during the programming sequence. Red buttonssignify an erratic pattern in the data entry, such as the “cancel” and“return to main menu” buttons. Of course, these colors can be replacedby other display attributes, such as intensity, underline, reversevideo, blinking and pixel dithering pattern, in addition to the use ofvarious fonts. Such a situation would include a monochrome monitor ordisplay.

The date may be entered in the form of a calendar rather than as numbers(i.e., “9/6/91”). This calendar method is advantageous because users maywish to input date data in one of three ways: day of the week, dayrelative to the present, and day of the month. The present method allowsthe current date to be highlighted, so that the calendar may be used toeasily enter the absolute day, absolute date, and relative day. Further,the choices “today” and “tomorrow”, the most frequently used relativerecording times, are included in addition to a month-by-month calendar.This information is provided to avoid an unnecessary waste of time anduser frustration. Thus, another aspect of the present invention is toprovide a partially redundant interactive display input system whichallows, according to the highest probability, the choices to beprominently displayed and easily available, in addition to allowingrandom access to all choices.

The present device allows common user mistakes to be recognized andpossibly addressed, such as the confusion between 12:00 PM and 12:00 AMwith midnight and noon, respectively. Therefore, the options of “noon”and “midnight” are provided in addition to a direct numeric clock input.When entering time information, leading zeros need not be entered, andsuch information may be entered in either fashion.

The criteria for system acceptance of input depends on how manykeystrokes are required on the screen. If only one keystroke is requiredto complete input of the information, upon depressing the key, theprogramming sequence will continue. If more than one keypress isrequired, the user must depress the “OK” button to continue programming.This context sensitive information entry serves to avoid unnecessaryinput.

An on-line “help” system and on-line feedback is preferably provided tothe user throughout various aspects of the interface. Other featuresinclude minimizing the number of keypresses required to program thedevice. These features, together with other aspects of the presentinvention allow the user to achieve a greater efficiency with the inputdevice than with prior art devices.

The interface of the present invention applied to a VCR controlpreferably comprises a virtual keypad entry device (i.e. arepresentation of an array of choices), a directional input control fora cursor on a display screen, and selection buttons. The input devicehas an input corresponding to a direction of movement relative to thecursor position. Thus, since the present input device seeks to minimizethe physical control elements of the human interface device, the displayelements for a preferred embodiment of the present interface include:

1. number keys 0-9.

2. enter key.

3. cancel key.

4. status indicator.

5. return to menu option button.

6. program type indicator: program once, program once a week, programMonday-Friday, program everyday.

7. Day indicators: 7 week days, today, tomorrow.

8. Noon and midnight choices.

9. Help button.

10. Main menu options: Review, Enter new recording time, Set time, Setdate.

11. Timer button.

12. Power button.

13. AM/PM choices.

14. 31 day calendar.

15. 12 month Choices.

16. 3 tape speed choices.

User dissatisfaction is generally proportionate to the length of “searchtime,” the time necessary in order to locate and execute the nextdesired function or instruction. Search time may be minimized by theinclusion of up to a maximum of 4-8 choices per screen and by use ofconsistent wording and placement of items on the display.

The present invention proceeds from the understanding that there are anumber of aspects of a programmable interface that are desirable:

First, users should be able to operate the system successfully, withoutwide disparities in time. It should take, e.g., a normal personinteracting with a VCR interface, less than seven minutes to set thetime and two programs. Searching time spent in setting the clock,programming, getting into the correct mode, and checking whether or notthe VCR is set correctly should be kept to a minimum through theappropriate choices of menu layout and the presentation of availablechoices.

Second, programming should be a stand-alone process, and not require aninstruction manual. A help system should be incorporated in theinterface. Word choices should be understandable, with a reduction inthe use of confusing word terminology. Error messages should beunderstandable. The system should provide the ability to cancel, changeor exit from any step.

Third, the system should provide on-screen understandable information,with adequate visual feedback. The displays should be consistent. Colorcoding should be employed, where applicable, using, e.g. blue—new input;red—error condition; yellow—static, unchanged value. Layouts should belogical, and follow a predictable pattern. There should be a maximum of4-8 choices per screen to minimize searching time. Keys should belabeled with text rather than with ambiguous graphics. However, acombination of both may be preferable in some cases.

Fourth, steps required to complete tasks should be simple, require ashort amount of time and not create user frustration. The system shouldguide the user along a decision path, providing automatic sequencing ofsteps. The most frequently used choices should be provided as defaults,and smart screens may be employed. The learning curve should beminimized through the use of easily understandable choices. As a userbecomes more sophisticated, the interface may present more advancedchoices.

Fifth, there should be a reminder to set the timer and to insert thetape once the programming information is entered. This reminder may alsobe automated, to eliminate the commonly forgotten step of setting thetimer, so that the VCR automatically sets the timer as soon as thenecessary information is entered and a tape is inserted. Once theprogram is set in memory, a message should appear if a tape is notinserted. If the VCR is part of a “jukebox” (automatic changer), thetape may be automatically loaded. The VCR should preferably turn on whena tape is inserted. In addition, users should also be able to controlthe VCR with a Power button.

Sixth, the VCR should be programmable from both the remote device andthe control panel.

Seventh, each operation should require only one keypress, if possible,or otherwise reduce the number of keypresses required. There should be a12 hour clock, not a 24 hour clock. There should be an on-screen keypadwith entry keys, not “up” and “down” selector keys, allowing for thechoice of specific day or time entry. There should be a “start” and a“stop” recording time, rather than “start” time and “length of program”or duration exclusively. The number of buttons on the remote controlshould be minimized so that as few buttons as are required are provided.The input device should provide for the direct manipulation of screenelements. A menu driven interface should be provided.

The interface of the present invention provides an automatic sequencingof steps which does not normally let the user think the previous step iscomplete. This is shown schematically in FIG. 16. In this manner,important steps will not be inadvertently omitted. Upon entering theprogramming sequence, if the current date or time is not set, theinterface will prompt the user to enter this information. Thereafter,the interface will normally default to the main menu, the mostfrequently used first screen. Thus, the interface of the presentinvention is adaptive, in that its actions depend on the current stateof the device, including prior programming or use of the device by theuser. It can be appreciated that this adaptive behavior can be extendedto include extended “intelligence”. For example, if the device issimilarly programmed on a number of occasions, then the default setupmay be adapted to a new “normal” program mode. Further, the apparatuscould provide multiple levels of user interface, e.g. beginner,intermediate, and advanced, which may differ for various functions,based on the behavior of the user. This user interface level determiningfeature extraction system is shown diagrammatically in FIG. 18. Incontrast, prior art interfaces that have different user interfacelevels, allow the user to explicitly choose the interface level, whichwill then be used throughout the system until reset.

The present system allows discrete tasks to be conducted more quickly,more efficiently, with reduced search time and with fewer errors thanprior art systems.

EXAMPLE 2

Serial Recording Medium Index

In a preferred embodiment of the present invention, in a VCR, in orderto track the content of the tape, a directory or a catalog is recorded,preferably digitally, containing the programming information, as well asadditional information about the recorded programs, in a header, i.e.,at the beginning of the tape, or at other locations on the tape. Thedevice may also catalog the tape contents separately, and based on anidentification of the tape, use a separately stored catalog. A preferredformat for storing information is shown in FIG. 19.

Thus, if there are a number of selections on the tape, the entirecontents of the tape could be accessible quickly, without the need forsearching the entire tape. In a sequential access medium, the tapetransport apparatus must still shuttle to the location of the desiredmaterial, but it may do so at increased speeds, because there is no needto read the tape once the location is determined; after the tapetransport nears the desired spot, the tape may be slowed or preciselycontrolled to reach the exact location.

The tape read and drive system is shown schematically in FIG. 20. Thealgorithm used in the final stage of approach to the desired portion ofthe tape or other recording medium may incorporate a control employingFuzzy logic, Neural Networks, mathematical formulae modeling the system(differential equations) in a Model-based system, aProportional-Differential-Integral (PID) system, or a controlleremploying an algorithm of higher order, or other known control methods.

If a selection is to be recorded over, the start and stop locationswould be automatically determined from the locations already indicatedon the tape. Further, this information could be stored in memory device(which reads a catalog or index of the tape when a new tape is loaded)or non-volatile memory device (which stores information relating toknown tapes within the device) or both types of memory in the VCR, sothat an index function may be implemented in the VCR itself, without theneed to read an entire tape. Optionally, a printer, such as a thermallabel printer (available from, e.g. Seiko Instruments, Inc.), attachedto the device, could be available to produce labels for the tapes,showing the index, so that the contents of a tape may be easilyindicated. A label on the tape may also include a bar code ortwo-dimensional coding system to store content or characterizationinformation. The stored identification and index information is thusstored in a human or machine readable form.

These contents, or a list of contents, need not necessarily be manuallyentered by the user or created by the apparatus, rather, these may bederived from published data or a database, data transmitted to thecontrol, and/or data determined or synthesized by the control itself.For example, broadcast schedules are available in electronic or machinereadable form, and this information may be used by the apparatus.

EXAMPLE 3

Serial Data Medium Index

Another aspect of the present invention relates to the cataloging andindexing of the contents of a storage medium. While random access medianormally incorporate a directory of entries on a disk, and devices suchas optical juke boxes normally are used in conjunction with softwarethat indexes the contents of the available disks, serial access massstorage devices, such as magnetic tape, do not usually employ an index;therefore, the entire tape must be searched in order to locate aspecific selection.

In the present invention, an area of the tape, preferable at thebeginning of the tape or at multiple locations therein, is encoded tohold information relating to the contents of the tape. This encoding isshown in FIG. 19, which shows a data format for the information. Thisformat has an identifying header 1901, a unique tape identifier 1902, anentry identifier 1903, a start time 1904, an end time 1905 and/or aduration 1906, a date code 1907, a channel code 1908, descriptiveinformation 1909 of the described entry, which may include recordingparameters and actual recorded locations on the tape, as well as a titleor episode identifying information, which may be a fixed or variablelength entry, optionally representative scenes 1910, which may beanalog, digital, compressed form, or in a form related to the abstractcharacterizations of the scenes formed in the operation of the device.Finally, there are error correcting codes 1911 for the catalog entry,which may also include advanced block encoding schemes to reduce theaffect of non-Gaussian correlated errors which may occur on video tape,transmission media and the like. This information is preferably amodulated digital signal, recorded on, in the case of Hi-Fi VHS, one ormore of the preexisting tracks on the tape, including the video,overscan area, Audio, Hi-Fi stereo audio, SAP or control tracks. Itshould be noted that an additional track could be added, in similarfashion to the overlay of Hi-Fi audio on the video tracks of Hi-Fi VHS.It is also noted that similar techniques could be used with Beta format,8 mm, or other recording systems, to provide the necessary indexingfunctions.

Digital data may also be superimposed as pseudonoise in the imageinformation, or as other information intermixed or merged with the videoinformation.

The recording method is preferable a block encoding method with errorcorrection within each block, block redundancy, and interleaving.Methods are known for reducing the error rate for digital signalsrecorded on unverified media, such as videotape, which are subject toburst errors and long term non-random errors. Such techniques reduce theeffective error rate to acceptable levels. These are known to thoseskilled in the art and need not be discussed herein in detail. Astandard reference related to this topic is Digital Communications byJohn G. Proakis, McGraw-Hill (1983). The digital data recording schemeis best determined according to the characteristics of the recordingapparatus. Therefore, if an, e.g. Sony Corporation helical scanrecording/reproducing apparatus was employed, one of ordinary skill inthe art would initially reference methods of the Sony Corporationinitially for an optimal error correcting recording scheme, which areavailable in the patent literature, in the U.S., Japan, andinternationally, and the skilled artisan would also review the knownmethods used by other manufacturers of digital data recording equipment.Therefore, these methods need not be explained herein in detail.

The catalog of entries is also preferably stored in non-volatile memory,such as hard disk, associated with the VCR controller. This allows therandom selection of a tape from a library, without need for manuallyscanning the contents of each tape. This also facilitates the randomstorage of recordings on tape, without the requirement of storingrelated entries in physical proximity with one another so that they maybe easily located. This, in turn, allows more efficient use of tape,because of reduced empty space at the end of a tape. The apparatus isshown schematically in FIG. 20, in which a tape drive motor 2001,controlled by a transport control 2002, which in turn is controlled bythe control 2003, moves a tape 2005 past a reading head 2004. The outputof the reading head 2004 is processed by the amplifier/demodulator 2006,which produces a split output signal. One part of the output signalcomprises the analog signal path 2007, which is described elsewhere. Adigital reading circuit 2008 transmits the digital information to adigital information detecting circuit 2009, which in turn decodes theinformation and provides it to the control 2003.

In order to retrieve an entry, the user interacts with the sameinterface that is used for programming the recorder functions; however,the user selects different menu selections, which guide him to theavailable selections. This function, instead of focusing mainly on theparticular user's history in order to predict a selection, would analyzethe entire library, regardless of which user instituted the recording.Further, there would likely be a bias against performing identically themost recently executed function, and rather the predicted function wouldbe an analogous function, based on a programmed or inferred userpreference. This is because it is unlikely that a user will perform anidentical action repeatedly, but a pattern may still be derived.

It is noted that the present library functions differ from the prior artVHS tape index function, because the present index is intelligent, anddoes not require the user to mark an index location and explicitlyprogram the VCR to shuttle to that location. Rather, the index iscontent based. Another advantage of the present library function is thatit can automatically switch media and recording format, providing anadaptive and/or multimode recording system. Such a system might be used,for example, if a user wishes to record, e.g., “The Tonight Show WithJohnny Carson” in highly compressed form, e.g. MPEG-2 at 200:1compression, except during the performance of a musical guest, at whichtime the recording should have a much lower loss, e.g., MPEG-2 at 20:1,or in analog format uncompressed. A normal VCR could hardly be used toimplement such a function even manually, because the tape speed (theanalogy of quality level) cannot generally be changed in mid recording.The present system could recognize the desired special segment, recordit as desired, and indicate the specific parameters on the informationdirectory. The recorded information may then be retrieved sequentially,as in a normal VCR, or the desired selection may be preferentiallyretrieved. If the interface of the present invention is set toautomatically record such special requests, the catalog section wouldthen be available for the user to indicate which selections wererecorded based upon the implicit request of the user. Because theinterface has the ability to characterize the input and record thesecharacterizations in the index, the user may make an explicit requestdifferent from the recording criteria, after a selection has beenrecorded. The controller would then search the index for matchingentries, which could then be retrieved based on the index, and without amanual search of the entire tape. Other advantages of the present systemare obvious to those of ordinary skill in the art.

A library system is available from Open Eyes Video, called “SceneLocator”, which implements a non-intelligent system for indexing thecontents of a videotape. See NewMedia, November/December 1991, p. 69.

It is noted that, if the standard audio tracks are used to record theindexing information, then standard audio frequency modems andrecording/receiving methods are available, adapted to record or receivedata in half-duplex mode. These standard modems range in speed from 300baud to about 64 kilobits per second, e.g. v.29, v.17, v.32, v.32bis,v.34, v.90, v.91, etc. While these systems are designed for dial-uptelecommunications, and are therefore are designed for the limited datarates available from POTS. These are limited to a slower speed thannecessary and incorporate features unnecessary for closed systems, theyrequire a minimum of design effort and the same circuitry may bemultiplexed and also be used for telecommunication with an on-linedatabase, such as a database of broadcast listings, discussed above. Itshould be noted that a full-duplex modem should be operated in halfduplex mode when reading or recording on a media, thus avoiding thegeneration of unnecessary handshaking signals. Alternatively, a fullduplex receiver may be provided with the resulting audio recorded. Aspecially programmed receiver may extract the data from the recording.DTMF codes may also be employed to stored information.

The Videotext standard may also be used to record the catalog orindexing information on the tape. This method, however, if used whiledesired material is on the screen, makes it difficult (but notimpossible) to change the information after it has been recorded,without re-recording entire frames, because the videotext uses the videochannel, during non-visible scan periods thereof. The video recordingsystem according to the present invention preferably faithfully recordsall transmitted information, including SAP, VAR, close caption andvideotext information, which may be used to implement the variousfunctions.

The use of on-line database listings may be used by the presentinterface to provide information to be downloaded and incorporated inthe index entry of the library function, and may also be used as part ofthe intelligent determination of the content of a broadcast. Thisinformation may further be used for explicitly programming the interfaceby the user, in that the user may be explicitly presented with theavailable choices available from the database.

EXAMPLE 4

Controlled Encryption and Accounting System

The present invention also allows for scrambling, encryption and lockingof source material, and the receiving device selectively implements aninverse process or a partial inverse process for descrambling,decryption or unlocking of the material, much as the Videocipher seriessystems from General Instruments, and the fractal enciphering methods ofEntertainment Made Convenient² Inc. (EMC², and related companies, e.g.,EMC³, and Iterated Systems, Inc. The present invention, however, is notlimited to broadcasts, and instead could implement a system for bothbroadcasts and prerecorded materials. In the case of copying from onetape to another, such a system could not only provide the hereinmentioned library functions of the present invention according toExample 2, it could also be used to aid in copy protection, serial copymanagement, and a pay-per-view royalty collection system.

Such a system could be implemented by way of a telecommunicationfunction incorporated in the device, shown as block 1808 of FIG. 18, oran electronic tag which records user activity relating to a tape or thelike. Such tags might take the form of a smart card, PCMCIA device, orother type of storage device. A royalty fee, etc., could automaticallybe registered to the machine either by telecommunication or registrywith the electronic tag, allowing new viewer options to be provided ascompared with present VCR's.

Numerous digital data encryption and decryption systems are known. Theseinclude DES, “Clipper”, elliptic key algorithms, public key/private key(RSA, etc.), PGP, and others. Digital encryption allows a sender toscramble a message so that, with an arbitrary degree of difficulty, themessage cannot be determined without use of a decryption key.

An encrypted tape or other source material may be decrypted with adecryption key available by telecommunication with a communicationcenter, remote from the user, in a decryption unit, shown schematicallyas the decrypt unit 1806a of FIG. 18. Such an encryption/decryptionscheme requires special playback equipment, or at least equipment withdecryption functionality, and thus any usage or decrypted data may beregistered as a result of the requirement to receive a decryption key.The decryption unit may be part of an addressable remote unit forcontrol of the unit remotely.

During acquisition of the electronic decryption key, a VCR device of anembodiment of the present invention would indicate its identity orelectronic address, and an account is charged a fee for such use. Thenegotiation for the electronic key is also preferably encrypted. Inaddition, the decryption key may be specific for a particular decoder.Such a system could also be used for controlled access software, forexample for a computer, wherein a remote account is charged for use ofthe software. Information communication may be through the Internet orthrough an on-line service such as America Online or Compuserve.

Such a system differs from the normal hardware “key” or “dongle” (devicewhich attaches to standard hardware port for authentication and usagelimitation) because it requires on-line or electronic access for anencryption key, which may offer different levels of use. It also differsfrom a call-in registration, because of the automatic nature of thetelecommunication. This presently described system differs from normalpay-per-view techniques because it allows, in certain instances, theuser to schedule the viewing. Finally, with an encryption functionimplemented in the VCR, the device allows a user to create anddistribute custom “software” or program material. In addition, thepresent controller could then act as the “telecommunication center” andauthorize decryption of the material.

If the source signal is in digital form, a serial copy management schemesystem is preferably implemented.

The present invention is advantageous in this application because itprovides an advanced user interface for creating a program (i.e. asequence of instructions), and it assists the user in selecting from theavailable programs, without having presented the user with a detaileddescription of the programs, i.e., the user may select the choice basedon characteristics rather than literal description.

In the case of encrypted program source material, it is particularlyadvantageous if the characterization of the program occurs withoutcharging the account of the user for such characterization, and onlycharging the account if the program is viewed by the user. The user maymake a viewing decision based on the recommendation of the interfacesystem, or may review the decision based on the title or description ofthe program, or after a limited duration of viewing. Security of thesystem could then be ensured by a two level encryption system, whereinthe initial decryption allows for significant processing, but notcomfortable viewing, while the second level of decryption allowsviewing, and is linked to the accounting system. Alternatively, thedecryption may be performed so that certain information, less than theentirety, is available in a first decryption mode, while otherinformation comprising the broadcast information is available in asecond decryption mode.

The transmission encryption system may be of any type, but for sensitivematerial, i.e. where mere distortion of the material (e.g., loss ofsynchronization information and phase distortion) would be insufficient,an analog multiple subband transform, with spread spectrum band hoppingand digital encryption of various control signals, would provide asystem which would be particularly difficult for the user to viewwithout authorization, and could be effectively implemented withconventionally available technology. The fractal compression andencryption of the EMC² and Iterated Systems, Inc. system is alsopossible, in instances where the broadcast may be precompressed prior tobroadcast and the transmission system supports digital data. Of course,if a digital storage format is employed, a strict digital encryptionsystem of known type may be used, such as those available from RSA. Theimplementation of these encryption systems is known to those skilled inthe art. These may include the National Bureau of Standards (NBS),Verifiable Secret Sharing (VSS) and National Security Agency (NSA)encryption standards, as well as various proprietary standards.

EXAMPLE 5

User Interface

In one embodiment of the present invention, the apparatus comprises aprogram entry device for a VCR or other type of media recording system.The human interface element has an infrared device to allow wirelesscommunication between the human interface device and the VCR apparatusproper. The human interface device also includes a direct-manipulationtype input device, such as a trackball or joystick. Of course it isunderstood that various known or to-be developed alternatives can beemployed, as described above.

It is noted that many present devices, intended for use in computershaving graphic interfaces, would advantageously make use of an inputdevice which is accessible, without the necessity of moving the user'shands from the keyboard. Thus, for example, Electronic Engineering Times(EET), Oct. 28, 1991, p. 62, discloses a miniature joystick incorporatedinto the functional area of the keyboard. This technique is directed ata different aspect of user interaction with a programmable device thancertain preferred embodiments of the present invention, in that theinput device does not have a minimal number of keys. While the devicedisclosed in EET is intended for use in a full function keyboard, thepreferred embodiment of the present invention is directed towards theminimization of the number of keys and avoidance of superfluous keys byprovision of a pointing device. Of course, the present invention couldbe used with a full function input device, where appropriate, and thejoystick of EET (Oct. 28, 1991, p. 62) would be suitable in this case.

The interface of the present invention studies the behavior and moods ofthe user, in context, during interactions to determine the expected userlevel of that user as well as the preferences of the user. These usercharacteristics may change over time and circumstances. This means thatthe system studies the interaction of the user to determine the skill ofthe user or his or her familiarity with the operation and functionalityof the system. By determining the skill of the user, the system mayprovide a best compromise. The purpose of this feature is to provide atailored interface adapted to the characteristics of the user, thusadaptively providing access to various features in a hierarchical mannersuch that a most likely feature to be used is more easily accessiblethan an unlikely feature, but that features can generally be accessedfrom all or most user levels. The user level analysis also allows thesystem to teach the user of the various functions available,particularly when it becomes apparent that the user is being inefficientin the use of the system to perform a given task. Therefore, the menustructure may also be adaptive to the particular task being performed bythe user. When combined with the user level analysis feature, the userefficiency feature will provide a preferable interface, with reducedlearning time and increased usability for a variety of users.

Thus, an important concept is that the system has at least one objecthaving a plurality of functions, certain of which are unnecessary or arerarely used for various applications or in various contexts, while theseare used with greater frequency in other contexts. Further, based uponpredetermined protocols and learned patterns, it is possible to predictwhich functions will be used and which will not be used.

Therefore, the system, upon recognizing a context, will reconfigure theavailability or ease of availability of functions and allow varioussubsets to be used through “shortcuts”. Thus, to some extent, theinterface structure may vary from time to time based upon the use of thesystem. The prior art apparently teaches away from this concept, becauseit is believed to prevent standardization, limits the “recordability” ofmacros and/or instruction sheets for casual users and limits theavailability of technical support. Each of these can be addressed, tosome extent by the availability of a default mode (so that users canaccess all information), and because the interface is self-simplifyingin case of difficulty. However, forcing all users to always work in adefault mode limits the improvements in productivity that may be gainedby a data-sensitive processing system, and hence this standardizationfor its own sake is rejected by the present invention.

The improvements to be gained by using an intelligent data analysisinterface for facilitating user control and operation of the system aremore than merely reducing the average number of keystrokes or time toaccess a given function. Initial presentation of all availableinformation to a new user might be too large an information load,leading to inefficiency, increased search time and errors. Rather, theimprovements arise from providing a means for access of and availabilityto functions not necessarily known to the user, and to therefore improvethe perceived quality of the product.

The system to determine the sophistication of the user includes a numberof storage registers, for storing an analysis of each act for each user.A given act is represented in a plurality of the registers, and aweighting system to ensure that even though an act is represented in anumber of registers, it is not given undue emphasis in the analysis.Thus, each act of the user may be characterized in a number of ways, andeach characteristic stored in an appropriate register, along with aweighting representing an importance of the particular characteristic,in relation to other identified characteristics and in relation to theimportance of the act as a whole. The act is considered in context, andtherefore, the stored information relates to the act, the sequence ofacts prior to the act, acts of the user occur after the act, the resultsof the sequence of acts which include the act, and characteristics ofthe user which are not “acts”, but rather include timing, mouse pathefficiency, and an interaction with other users.

An apparatus for performing a path information or efficiency determiningfunction is shown schematically in FIG. 18, and in more detain in FIG.21. Thus, for example, if a characteristic of the user is an unsteadyhand while using the cursor control device, e.g. mouse, producing a highfrequency or oscillating component, the existence of this characteristicis detected and quantified by the high frequency signal componentdetector 2112, and, depending on the amplitude, frequency and duration(e.g. path length), may also be detected by the path optimizationdetector 2105. Once this characteristic is detected and quantified, anadaptive filter may be applied by the main control 1806 to selectivelyremove the detected component from the signal, in order to improve thereliability of the detection of other characteristics and to determinethe intended act of the user.

It should be noted that the various characteristic filters preferablyact in “parallel” at each stage of the characteristic recognition,meaning that one characteristic is defined simultaneously with thedetection of other characteristics, which assists in resolvingambiguities, allows for parallel processing by a plurality of processingelements which improves real-time recognition speed, and allows aprobability-based analysis to proceed efficiently. Such a “parallel”computation system is included in a neural net computer, and ahardware-implementation of a neural net/fuzzy logic hybrid computer is apreferred embodiment, which allows fuzzy rules to be programmed toprovide explicit control over the functioning of the system. It ispreferred that a human programmer determine the basic rules of operationof the system, prior to allowing a back-propagation of errors learningalgorithm to improve and adapt the operation of the system.

The adaptive system implemented according to the present invention, bydetecting a user level, allows a novice user to productively interactwith the system while not unnecessarily limiting the use of the adaptiveinterface by an advanced user, who, for example, wishes to move thecursor quickly without the limiting effects of a filter which slowscursor response.

Another example of the use of an adaptive user interface level is a userwho repeatedly requests “help” or user instructions, through theexplicit help request detector 2115, which causes an output from thecurrent help level output 2102; such a user may benefit from anautomatic context-sensitive help system, however such a system mayinterfere with an advanced user, and is unnecessary in that case andshould be avoided. This adaptive user interface level concept is notlimited to a particular embodiment of the present invention, such as aVCR, and in fact, may be broadly used wherever a system includes aninterface which is intended for use by both experienced andinexperienced users. This differs from normal help systems which must bespecifically requested, or “balloon help” (Apple Computer, MacintoshSystem 7.0, 7.1, 7.5) which is either engaged or disengaged, but notadaptive to the particular situation based on an implicit request orpredicted need. In the case of a single user or group of users, theinterface could maintain a history of feature usage for each user, as inthe past user history block 2107, and provide a lower user interfacelevel for those features which are rarely used, and therefore lessfamiliar to the user, through the current user level output 2101.

It should be noted that the present system preferably detects anidentity of a user, and therefore differentiates between different usersby an explicit or implicit identification system. Therefore, the systemmay accumulate information regarding users without confusion orintermingling.

EXAMPLE 6

VCR Programming Preference Prediction

The device according to the present invention is preferably intelligent.In the case of a VCR, the user could also input characteristics of theprogram material that are desired, and characteristics of that programmaterial which is not desired. The device would then, over time, monitorvarious broadcast choices, and determine which most closely match thecriteria, and thus be identified. For example, if the user prefers“talk-shows”, and indicates a dislike for “situation comedies”(“sitcoms”), then the device could scan the various available choicesfor characteristics indicative of one or the other type of programming,and perform a correlation to determine the most appropriate choice(s). Asitcom, for example, usually has a “laugh track” during a pause innormal dialogue. The background of a sitcom is often a confined space (a“set”), from different perspectives, which has a large number of “props”which may be common or unique. This set and the props, however, may beenduring over the life of a show.

A talk-show, on the other hand, more often relies on actual audiencereaction (possibly in response to an “applause” sign), and notprerecorded or synthesized sounds. The set is simple, and the broadcastoften shows a head and neck, or full body shot with a bland background,likely with fewer enduring props. A signal processing computer,programmed for audio and/or video recognition, is provided todifferentiate between at least the two types with some degree ofefficiency, and with a possibly extended sampling time, have arecognition accuracy, such that, when this information is integratedwith other available information, a reliable decision may be made. Therequired level of reliability, of course, will depend on the particularapplication and a cost-benefit analysis for the required system toimplement the decision-making system.

Since the system according to the present invention need not displayperfect accuracy, the preferred embodiment according to the presentexample applies general principles to new situations and receives useror other feedback as to the appropriateness of a given decision. Basedon this feedback, subsequent encounters with the same or similar datasets will produce a result which is “closer” to an optimal decision.Therefore, with the aid of feedback, the search criterion would beimproved. Thus, a user could teach the interface through trial and errorto record the desired broadcast programs. Thus, the presently describedrecognition algorithms may be adaptive and learning, and need not applya finite set of predetermined rules in operation. For such a learningtask, a neural network processor may be implemented, as known in theart.

The feature extraction and correlation system according to the presentinvention is shown in FIG. 22. In this figure, the multimedia input,including the audio signal and all other available data, are input inthe video input 2201. The video portion is transferred to a frame buffer2202, which temporarily stores all of the information. All otherinformation in the signal, including audio, VIR, videotext, closecaption, SAP (second audio program), and overscan, is preferably storedin a memory, and analyzed as appropriate. The frame buffer 2202 may havean integral or separate prefiltering component 2203. The filteredsignal(s) are then passed to a feature extractor 2204, which divides thevideo frame into a number of features, including movement, objects,foreground, background, etc. Further, sequences of video frames areanalyzed in conjunction with the audio and other information, andfeatures relating to the correlation of the video and other information,e.g., correlation of video and audio, are extracted. Other informationis also analyzed and features extracted, e.g., audio and close caption.All extracted features relating to the multimedia input are then passedto a transform engine or multiple engines in parallel, 2205. Thesetransform engines 2205 serve to match the extracted features withexemplars or standard form templates in the template database 2206.

It should be noted that even errors or lack of correlation betweencertain data may provide useful information. Therefore, a mismatchbetween audio and close caption or audio and SAP may be indicative ofuseful information. For non-video information, exemplars or templatesare patterns which allow identification of an aspect of the signal bycomparing the pattern of an unidentified signal with the stored pattern.Thus, the voice patterns of particular persons and audio patterns ofparticular songs or artists may be stored in a database and employed toidentify a source signal.

The transformed extracted features and the templates are then correlatedby a correlator or correlators 2207. The parallelization ofimplementation of the transforms and correlators serves to increase therecognition speed of the device. It should be understood thatappropriate systems for parallelization are known in the art. Forexample, the TMS 320C80, also known as the TI MVP (Texas Instrumentsmultimedia video processor) contains four DSP engines and a RISCprocessor with a floating point unit on a single die. A board includinga TMS 320C80 is available from General Imaging Corp., Billerica Mass.,the S/IP80, which may be programmed with ProtoPIPE. In addition, a boardincluding a TMS 320C80 is also available from Wintriss EngineeringCorp., San Diego, Calif. Multiple MVP processors may also beparallelized for additional computing power. The MVP may be used toanalyze, in parallel, the multimedia input signal and correlate it withstored patterns in a database. In this context, correlation does notnecessarily denote a strict mathematical correlation, but ratherindicates a comparison to determine the “closeness” of an identifiedportion of information with an unidentified portion, preferablyincluding a reliability indicator as well. For neural network-basedprocessing, specific hardware accelerators also available, such as fromNestor, Inc. and Intel. Therefore, since there may be multiplerecognizable aspects of the unidentified data, and various degrees orgenericness of the characteristic recognized, it is preferred that atthis initial stage of the recognition process that the output of thecorrelators 2207 be a data set, e.g. a matrix, series of pointers, orother arrangement, so that sufficient information is available forhigher level processing to allow application of an appropriate decisionprocess. Of course, if the characteristic to be detected is simple andwell defined, and the decision-making process may be implemented with asimple correlation result, then a complex data set output is notrequired. In fact, the output of the correlator may have a number ofdifferent forms, based on the context of the recognition process.

If, for example, an exact match to an entire frame is sought, partialmatch information is not particularly useful, and is ignored in thisprocess. (Of course, since the system is “self-learning”, the processingresults may be maintained and analyzed for other purposes). If thesystem, on the other hand, is analyzing novel data, a full analysiswould likely be necessary including partial results and low correlationresults.

The outputs of the correlators are input into an adaptive weighingnetwork 2208, to produce a probability of a match between a givenfeature and a given template. The recognition is completed in anidentifier 2209, which produces a signal identifying one or more objectsin the video frame input. The identifier 2209 also has an output to thetemplate database 2206, which reinforces the recognition by providingfeedback; therefore, if the same object appears again, it will be moreeasily recognized. The template database 2206 therefore also has aninput from the feature extractor 2204, which provides it withinformation regarding the features recognized. It is also noted that, inaddition to allowing recognition, the parallel transform engines 2205,correlators 2207, and adaptive weighing network 2208 also allows thesystem to ignore features that, though complex, do not aid inrecognition.

For example, during dialogue, the soundtrack voice may correlate withthe mouth movements. Thus, the mouth movements aid little inrecognition, and may be virtually ignored, except in the case where aparticular person's mouth movements are distinctive, e.g., Jim Nabors(“Gomer Pyle”), and Tim Curry (“Rocky Horror Picture Show”). Thus, thecomplexity and parallelism in the intermediate recognition stages mayactually simplify the later stages by allowing more abstract features tobe emphasized in the analysis. Animation poses a special example whereaudio and image data may be separated, due to the generallynon-physiologic relation between the image and soundtrack.

The pattern recognition function of the present invention could be used,in a VCR embodiment according to the present invention to, e.g., to editcommercials out of a broadcast, either by recognition of characteristicspresent in commercials, in general, or by pattern recognition ofspecific commercials in particular, which are often repeated numeroustimes at various times of the day, and on various broadcast channels.Therefore, the system may acquire an unidentified source signal, whichmay be, for example, a 30 second segment, and compare this with adatabase of characteristics of known signals. If the signal does notmatch any previously known or identified signals, it is then subject toa characterization which may be the same or different than thecharacterization of the identified signals. The characterizations of theunidentified signal are then compared to characteristics to berecognized. If the unidentified signal meets appropriate criteria, apresumptive generic characterization is made. This characterization ispreferably confirmed by a user later, so that a positively identifiedsignal is added to the database of identified signals; however, undercertain circumstances no confirmation is required.

Certain media present a recognizable audio or video cue when acommercial break has ended. (E.g. often sports events, such as theOlympic Games, will have theme music or distinctive images). The presentdevice need not respond immediately to such cues, and may incorporate adelay, which would store the information while a decision is being made.In the case of a video tape, the delay may be up to the time between thetime of recording and the time of playback. Further, the temporarystorage medium may be independent of the pattern recognition system.Thus, a system provided according to the present invention may actuallyinclude two independent or semi-independent data streams: the firstserving as the desired signal to be stored, retaining visually importantinformation, and the second providing information for storage relatingto the pattern recognition system, which retains information importantfor the recognition process, and may discard this information after thepattern recognition procedure is complete.

A system which provides a plurality of parallel data streamsrepresenting the same source signal may be advantageous because isallows a broadcast quality temporary storage, which may be analog innature, to be separate from the signal processing and patternrecognition stage, which may be of any type, including digital, optical,analog or other known types, which need only retain significantinformation for the pattern recognition, and therefore may be highlycompressed (e.g. lossy compression), and devoid of various types ofinformation which are irrelevant or of little importance to the patternrecognition functions. Further, the temporary storage may employ adifferent image compression algorithm, e.g. MPEG-4, MPEG-2 or MPEG-1,which is optimized for retention of visually important information,while the recognition system may use a compression system optimized forpattern recognition, which may retain information relevant to therecognition function which is lost in other compression systems, whilediscarding other information which would be visually important.Advantageously, however, the analysis and content transmission streamsare closely related or consolidated, such as MPEG-7 and MPEG-4.

In a particularly advantageous arrangement, the compression algorithm isintegral to the recognition function, preparing the data for the patternmatching and characterization, and therefore is optimized for highthroughput. According to this embodiment, the initial compression mayinclude redundant or uncompressed information, if necessary in order toachieve real-time or near real-time recognition, and, thus may actuallyresult in a larger intermediate data storage requirement than theinstantaneous data presented to the recognition system; however, theterm “compression”, in this case, applies to the long term or steadystate status of the device, and in a real-time recognition function, theamount of data stored for use in recognition is preferably less than thecumulative amount of data presented, except during the very initialstages of data acquisition and possibly rare peaks.

In the case where a high quality (low loss, e.g. broadcast quality)intermediate storage is employed, after a decision is made as to whetherthe data should be stored permanently or otherwise further processed ordistributed, the data may be transferred to the appropriate system orsubsystem of the apparatus. Alternatively, the high quality intermediatestorage is retained, and no further processing is performed. In eithercase, the purpose of this storage is to buffer the source data until thecomputational latency resolves any decisions which must be made.

According to one aspect of the present invention, the source image maybe compressed using the so called “fractal transform”, using the methodof Barnsley and Sloan, which is implemented and available as a hardwareaccelerator in product form from Iterated Systems, Inc., Norcross, Ga.,as the Fractal Transform Card (FTC) II, which incorporates eight fractaltransform integrated circuit chips, 1 MByte of Random Access Memory(RAM), and an Intel i80960CA-25 □P, and operates in conjunction withP.OEM™ (Iterated Systems, Inc., Norcross, Ga.) software, which operatesunder MicroSoft-Disk Operating System (MS-DOS). FTC-II hardwarecompression requires approximately 1 second per frame, while softwaredecompression on an Intel 80486-25 based MS-DOS computer, using “FractalFormatter” software, can be performed at about 30 frames per second,which allows approximately real time viewing. The Fractal Video Pro 1.5is a video codec for WIN, allowing software only playback at 15-30 fps,70-150 Kbytes/sec. This is a non-symmetrical algorithm, requiring moreprocessing to compress than to decompress the image. The FTC-IVCompression Accelerator Board is presently available.

This fractal compression method potentially allows data compression ofupwards of 2000:1, while still maintaining an aesthetically acceptabledecompressed image result. Further, since the method emphasizesstructural aspects of the image, as opposed to the frequencydecomposition used in DCT methods (JPEG, MPEG), elements of the fractalmethod could be used as a part of the image recognition system. Ofcourse, it should be appreciated that other fractal processing methodsare available and may be likewise employed.

Audio data is also compressible by means of fractal transforms. It isnoted that the audio compression and image recognition functions cannotbe performed on the FTC-II board, and therefore an alternate system mustbe employed in order to apply the pattern recognition aspects of thepresent invention. It should also be noted that an even more efficientcompression-pattern recognition system could be constructed by using thefractal compression method in conjunction with other compressionmethods, which may be more efficient under certain circumstances, suchas discrete cosine transform (DCT), e.g. JPEG or modified JPEG orwavelet techniques. Fractal compression systems are also available fromother sources, e.g. the method of Greenwood et al., Netrologic Inc., SanDiego, Calif. See also, Shepard, J. D., “Tapping the Potential of DataCompression”, Military and Aerospace Electronics, May 17, 1993, pp.25-27.

A preferred method for compressing audio information includes amodel-based compression system. This system may retain stored samples,or derive these from the data stream. The system preferably alsoincludes high-level models of the human vocal tract and vocalizations,as well as common musical instruments. This system therefore storesinformation in a manner which allows faithful reproduction of the audiocontent and also provides emphasis on the information-conveyingstructure of the audio signal. Thus, a preferred compression for audiosignals retains, in readily available form, information important in apattern recognition system to determine an abstract information content,as well as to allow pattern matching. Of course, a dual data streamapproach may also be applied, and other known compression methods may beemployed.

Because of the high complexity of describing a particular signal patternor group of audio or image patterns, in general, the system will learnby example, with a simple identification of a desired or undesiredpattern allowing analysis of the entire pattern, and extraction ofcharacteristics thereof for use in preference determination.

Barnsley and Sloan's method for automatically processing digital imagedata consisting of image information, disclosed in U.S. Pat. Nos.5,065,447 and 4,941,193, both expressly incorporated herein byreference, consists of the steps of storing the image data in the dataprocessor, then generating a plurality of uniquely addressable domainblocks from the stored image data, each of the domain blocksrepresenting a different portion of the image information such that allof the image information is contained in at least one of the domainblocks. A plurality of uniquely addressable mapped range blockscorresponding to different subsets of the stored image data are created,from the stored image data, with each of the subsets having a uniqueaddress. This step includes the substep of executing, for each of themapped range blocks, a corresponding procedure upon the one of thesubsets of the stored image data which corresponds to the mapped rangeblock. Unique identifiers are then assigned to corresponding ones of themapped range blocks, each of the identifiers specifying for thecorresponding mapped range block a procedure and an address of thecorresponding subset of the stored image data. For each of the domainblocks, the one of the mapped range blocks which most closelycorresponds according to predetermined criteria is selected. Finally,the image information is represented as a set of the identifiers of theselected mapped range blocks. This method allows a fractal compressionof image data. In particular, Drs. Barnsley and Sloan have optimized thematch of the domain blocks with the mapping region by minimizing theHausdorff distance. A decompression of the data precedes analogously inreverse order starting with the identifiers and the mapping regions toproduce a facsimile of the original image. This system is highlyasymmetric, and requires significantly more processing to compress thanto decompress. Barnsley and Sloan do not suggest a method for using thefractal compression to facilitate image recognition, which is a part ofthe present invention.

Basically, the fractal method proceeds from an understanding that realimages are made up of a plurality of like subcomponents, varying insize, orientation, etc. Thus, a complex block of data may be describedby reference to the subcomponent, the size, orientation, etc. of theblock. The entire image may thus be described as the composite of thesub-images. This is what is meant by iterative function systems, wherefirst a largest block is identified, and the pattern mapping isrepetitively performed to describe the entire image.

The Iterated Systems, Inc. FTC-II or FTC-IV board, if applied as a partof a system according to the present invention, is preferably used inconjunction with a frame-grabber board, such as Matrox, Quebec, Canada,Image-LC board, or a Data Translation DT1451, DT2651, DT2862, DT2867,DT2861 or DT2871, which may perform additional functions, such aspreprocessing of the image signal, and may be further used inconjunction with an image processing system, such as the DataTranslation DT2878. Of course, it should be understood that any suitablehardware, for capturing, processing and storing the input signals, up toand including the state of the art, may be incorporated in a systemaccording to the present invention without exceeding the scope hereof,as the present invention is not dependent on any particular subsystem,and may make use of the latest advances. For example, many modernsystems provide appropriate functionality for digital video capture,either uncompressed, mildly compressed, or with a high degree ofcompression, e.g., MPEG-2.

The Texas Instruments TMS320C80 provides a substantial amount ofcomputing power and is a preferred processor for certain computationallyintensive operations involving digital signal processing algorithms. Asystem employing a parallel TMS 320C40 processors may also be used. TheIntel Pentium series (or related processors from AMD, NationalSemiconductor, or other companies), DEC/Compaq Alpha, SPARC, or otherprocessors intended for desktop computing may, either individually or inmultiprocessor configurations, be used to process signals.

A pattern recognition database system is available from ExcaliburTechnologies, San Diego, Calif. Further, IBM has had pattern recognitionfunctionality available for its DB/2 database system, and has licensedExcalibur's XRS image retriever recognition software for DB/2. See, Lu,C., “Publish It Electronically”, Byte, September 1993, pp. 94-109. AppleComputer has included search by sketch and search by example functionsin PhotoFlash 2.0. See also, Cohen, R., “FullPixelSearch Helps UsersLocate Graphics”, MacWeek, Aug. 23, 1993, p. 77.

Image processing hardware and systems are also available from Alacron,Nashua N.H.; Coreco, St. Laurent, Quebec; Analogic, and others.

A fractal-based system for real-time video compression, satellitebroadcasting and decompression is also known from Iterated Systems, Inc.and Entertainment Made Convenient², Inc. (EMC²). In such a system, sincethe compressed signal is transmitted, the remote receiving system neednot necessarily complete decompression prior to the intelligent patternrecognition function of the present invention. This system alsoincorporates anti-copy encryption and royalty and accountingdocumentation systems. It is noted that the EMC² system does notincorporate the intelligent features of the present invention.

A preferred fractal-based system according to the present informationprovides the source data preprocessed to allow easy and efficientextraction of information. While much precharacterization informationmay be provided explicitly, the preferred system allows other, unindexedinformation to also be extracted from the signal. Further, the preferredsystem provides for an accounting system which facilitates pay-per-viewfunctions. Thus, the interface of the present invention could interactwith the standard accounting system to allow royalty-based recording orviewing, and possibly implement a serial-copy recording preventionsystem. Prior art systems require a user to explicitly select a program,rather than allow an intelligent system to assist in selection andprogramming of the device. The EMC² system is described in “EMC² PushesVideo Rental By Satellite”, Electronic Engineering Times, Dec. 2, 1991,p. 1, p. 98. See also, Yoshida, J., “The Video-on-demand Demand”,Electronic Engineering Times, Mar. 15, 1993, pp. 1, 72.

Fractal techniques may be used to store images on a writable massstorage medium, e.g. CD-ROM compatible. The present system may thus beused to selectively access data on the CD-ROM by analyzing the images,without requiring full decompression of the image data.

Wavelets hold promise for efficiently describing images (i.e.,compressing the data) while describing morphological features of theimage. However, in contrast to wavelet transforms which are not intendedto specifically retain morphological information, the selection of theparticular wavelet and the organization of the algorithm will likelydiffer. In this case, the transform will likely be more computationallycomplex and therefore slower, while the actual compression ratiosachieved may be greater.

Thus, one embodiment of the device according to the present inventionmay incorporate a memory for storing a program, before being transferredto a permanent storage facility, such as tape. Such a memory may includea hard disk drive, magnetic tape loop, a rewritable optical disk drive,or semiconductor memories, including such devices as wafer scale memorydevices. This is shown diagrammatically as the intermediate storage 2210of FIG. 22. The capacity of such a device may be effectively increasedthrough the use of image data compression, which may be proprietary or astandard format, i.e. MPEG-1, MPEG-2 (Motion Picture Experts Groupstandard employing DCT encoding of frames and interframe coding), MPEG-4(Motion Picture Experts Group standard employing DCT encoding of framesand interframe coding, as well as model-based encoding methods) JPEG(Joint Photographic Experts Group standard employing DCT encoding offrames), Px64 (Comité Consultatif International des Telegraph ettelephone (International telegraph and telephone consultative committee)(CCITT) standard H.261, videoconferencing transmission standard), DVI(Digital Video Interactive), CDI (Compact Disk Interactive), etc.

Standard devices are available for processing such signals, availablefrom 8×8, Inc., C-Cube, Royal Philips Electronics (TriMedia), and othercompanies. Image processing algorithms may also be executed on generalpurpose microprocessor devices.

Older designs include the Integrated Information Technology, Inc. (IIT,now 8×8, Inc.) Vision Processor (VP) chip, Integrated InformationTechnology Inc., Santa Clara, Calif., the C-Cube CL550B (JPEG) and CL950(MPEG decoding), SGS-Thompson ST13220, STV3200, STV3208 (JPEG, MPEG,Px64), LSI Logic L64735, L64745 and L64765 (JPEG) and Px64 chip sets,and the Intel Corp. i750B DVI processor sets (82750PB, 82750DB). Variousalternative image processing chips have been available as single chipsand chip sets; in board level products, such as the Super MotionCompression and Super Still-Frame Compression by New Media Graphics ofBillerica, Mass., for the Personal Computer-Advanced technology (PC-AT,an IBM created computer standard) bus; Optibase, Canoga Park, Calif.(Motorola Digital Signal Processor (DSP) with dedicated processor forMPEG); NuVista+from Truevision (Macintosh video capture and output); NewVideo Corp. (Venice, Calif.) EyeQ Delivery board for Macintosh NuBussystems (DVI); Intel Corp. ActionMedia II boards for Microsoft Windowsand IBM OS/2 in Industry Standard Adapter (ISA, the IBM-PC bus standardfor 8 (PC) or 16 bit (PC-AT) slots); Micro Channel Architecture (MCA)(e.g., Digital Video Interactive (DVI), Presentation Level Video (PLV)2.0, Real Time Video (RTV) 2.0) based machines; and as completeproducts, such as MediaStation by VideoLogic.

Programmable devices, including the Texas Instruments TMS320C80 MVP(multimedia video processor) may be used to process informationaccording to standard methods, and further provide the advantage ofcustomizability of the methods employed. Various available DSP chips,exemplary board level signal processing products and available softwareare described in more detail in “32-bit Floating-Point DSP Processors”,EDN, Nov. 7, 1991, pp. 127-146. The TMS320C80 includes four DSP elementsand a RISC processor with a floating point unit.

It is noted that the present interface does not depend on a particularcompression format or storage medium, so that any suitable format may beused. The following references describe various video compressionhardware: Kim, Y., “Chips Deliver Multimedia”, Byte, December 1991, pp.163-173; and Donovan, J., “Intel/IBM's Audio-Video Kernel”, Byte,December, 1991, pp. 177-202.

It should also be noted that the data compression algorithm applied forstorage of the received data may be lossless or lossy, depending on theapplication. Various different methods and paradigms may be used. Forexample, DCT (discrete cosine transform) based methods, wavelets,fractals, and other known methods may be used. These may be implementedby various known means. A compressed image may also be advantageouslyused in conjunction with the image recognition system of the presentinvention, as described above. In such a case, the compression systemwould retain the information most important in the recognition function,and truncate the unimportant information.

A further method of performing pattern recognition, especially of twodimensional patterns, is optical pattern recognition, where an image iscorrelated with a set of known image patterns represented on a hologram,and the product is a pattern according to a correlation between theinput pattern and the provided known patterns. Because this is anoptical technique, it is performed nearly instantaneously, and theoutput information can be reentered into an electronic digital computerthrough optical transducers known in the art. Such a system is describedin Casasent, D., Photonics Spectra, November 1991, pp. 134-140. See alsoreferences cited therein.

These optical recognition systems are best suited to applications wherean uncharacterized input signal frame is to be compared to a finitenumber of visually different comparison frames (i.e., at least one, withan upper limit generally defined by the physical limitations of theoptical storage media and the system for interfacing to the storagemedia), and where an optical correlation will provide usefulinformation. Thus, if a user wished to detect one of, e.g., “DavidLetterman”, “Jay Leno”, or “David Koppel”, a number of different planarviews, or holograms in differing poses, of these persons would be formedas a holographic correlation matrix, which could be superimposed as amultiple exposure, stacked in the width dimension, or placed in a planarmatrix, side by side. The detection system produces, from theuncharacterized input image and the holographic matrix, a wavefrontpattern that is detectable by photonic sensors.

It is preferred that if multiple holographic images of a particularcharacterization are employed, that they each produce a more similarresulting wavefront pattern than the holographic images of othercharacterizations, in order to enhance detection efficiency. The opticalpattern recognition method is limited in that a holographic image mustbe prepared of the desired pattern to be detected, and that opticallysimilar images might actually be of a different image, if thedifferences are subtle. However, this method may be used in conjunctionwith electronic digital pattern recognition methods, to obtain theadvantages of both. Methods are also known to electronically write animage to a holographic storage medium, thereby facilitating its use in ageneral-purpose image recognition system. Of course, the system may alsobe used to identify talk show guests, such as “Richard Gere” or “CindyCrawford”, or these same individuals in other contexts.

If image compression is used, once an image is compressed, it need notbe decompressed and returned to pixel, NTSC or other standardtransmission or format for storage on tape, and thus the compressedimage information may be stored in the same format as is present in thetemporary storage medium. Thus, the block labeled intermediateprocessing 2211 of FIG. 22 shows that the intermediate storage need notretain the information as received from the frame buffer 2202, and infact, may prepare it for the feature extractor 2204. In addition, thestorage medium itself need not be normal videotape (S-VHS, VHS, Beta, 8mm, Hi-8) and may be an adapted analog storage technique or a digitalstorage technique. Various magneto-optical recording techniques areknown, which can store between 128 MB (3½″) and around 5 GB (11″),uncompressed, which might be suitable for storing compressed digital oranalog information. Multilayer CD-ROM and short wavelength (e.g., blue)laser systems allow storage densities of about 3.5 to 10 Gbytes perdisk, allowing storage of over two hours of MPEG-2 encoded video.

It is also noted that the present technology could also be applied toany sort of mass storage, such as for a personal computer. In such acase, a characteristic of the computer file, which is analogous to thebroadcast program in temporary storage of a VCR, is classified accordingto some criteria, which may be explicit, such as an explicit header oridentifying information, or implicit, such as a document in letterformat, or a memorandum, as well as by words and word proximity. Inparticular, such a recognition system could differentiate variousclients or authors based on the content of the document, and these couldbe stored in different manners. The text analysis system of a text-basedcomputer storage system is analogous to the program classificationsystem of the VCR embodiment of the present invention. However, there isa further analogy, in that the VCR could incorporate optical characterrecognition of text displayed in the program material, employ voicerecognition, or directly receive text information as a part of a closedcaption or videotext system. Thus, the VCR device according to thepresent invention could recognize and classify programs based on textualcues, and make decisions based on these cues. This might also provide asimple method of discriminating program material, for example, if acommercial does not include close caption or Second Audio Program (SAP),while the desired program does, or vice versa, then a commercial couldbe discriminated from a program with very little computationalexpenditure.

EXAMPLE 7

VCR Interface

A particular VCR interface system according to one aspect of the presentinvention includes an internal clock, four program memory, and thecapability to display a graphical color interface. By providing the userwith the aforementioned features, this design is a unique implementationfor an instrument to be used for programming an event driven controllervia an interactive display. All information that the user needs isdisplayed on the screen to avoid or minimize the unnecessary searchingfor information. This information includes the current date and currenttime.

A simulation of the AKAI Inc. VCR VS303U (on-screen programming) and theinterface of the present invention, were tested to evaluate users'performances. The AKAI interface of the prior art, hereinafter referredto as the prior art interface, was chosen because users made the fewesterrors while using this machine, and no user quit while programming, ascompared to three other VCRs tested, a Panasonic (made by Matsushita,Inc.) PV4962 (Bar Coder), an RCA brand (formerly Radio Corporation ofAmerica, Inc.) VKP950 (on-screen programming), Panasonic brand (made byMatsushita Inc.) PV4700 (Display Panel).

The present embodiment was constructed and tested using HyperPAD™, arapid prototyping package for an IBM-PC Compatible Computer. It is, ofcourse obvious that the present embodiment could be incorporated in acommercial VCR machine by those skilled in the art, or be implemented onmany types of general purpose computers with output screens which allowon-screen feedback for the programming operation. Further, the system ofthe present embodiment can include a remote-control device whichcommunicates with a VCR through an infrared beam or beams, and can thusexert control over an infrared remote controlled VCR, or translate theprogramming information and communicate through an infrared remotecontrol, using the standard type infrared transmitter.

An IBM PC-AT compatible (MS-DOS, Intel 80286-10 MHz) computer was usedto test the two simulations. In order to simulate the use of a remotecontrol device in programming the VCR, an infrared device made by NView™was attached to the computer. This device came with a keyboard that wasused to “teach” a Memorex™ Universal Remote so that the desired actionscould be obtained. By using a universal remote, the computer could becontrolled by using a remote control.

The present embodiment incorporates a mouse input device. It isunderstood that a small trackball with a button for selection, mountedon a remote control may also be employed, and may be preferable incertain circumstances. However, a computer mouse is easily available,and the mouse and trackball data are essentially similar for the type oftask implemented by the user, with trackball performance being slightlyfaster. For daily use on a VCR however, a trackball would be a morepreferable input device because it does not require a hard, flatsurface, which is not always available to a user when programming a VCR,such as in the situation where a person is watching television whilesitting in a chair or sofa.

A Genius™ Mouse was used as the input device in the prototype of theinterface of the present invention. With the mouse, the user could viewall of the choices at once on the display screen, and then make aselection from the items on the screen by moving the cursor and thenpressing the left mouse button.

The interface of the present example focuses on attending to the user'sneeds, and the interface must be modified for each application. Byreducing the searching, learning times, and entry times, the mental loadis also minimized. Some tradeoffs are necessary as a result ofsubjective and objective data. Because of the difficulty in optimizing asingle interface design for all levels of users, a menu system was usedin an attempt to satisfy all these user types.

The interface of the present example reduced the number of incorrectrecordings by 50%. The severity of the errors is unimportant herebecause one wrong entry will cause an irretrievable mistake and the userwill not record the intended program. One study reported that faultyinputs, which lead to missing the program, can be reported by almostevery present day owner of a VCR.

EXAMPLE 8

Programmable Device Interface

It is also noted that the interface of the present invention need not belimited to audio-visual and multimedia applications, as similar issuesarise in various programmable controller environments. Such issues aredisclosed in Carlson, Mark A., “Design Goals for an Effective UserInterface”, Electro/82 Proceedings, 3/1/1-3/1/4; Kreifeldt, John, “HumanFactors Approach to Medical Instrument Design”, Electro/82 Proceedings,3/3/1-3/3/6; Wilke, William, “Easy Operation of Instruments by Both Manand Machine”, Electro/82 Proceedings, 3/2/1-3/2/4; Green, Lee, “ThermoTech: Here's a common sense guide to the new thinking thermostats”,Popular Mechanics, October 1985, 155-159; Moore, T. G. and Dartnall,“Human Factors of a Microelectronic Product: The Central HeatingTimer/Programmer”, Applied Ergonomics, 1983, Vol. 13, No. 1, 15-23; and“The Smart House: Human Factors in Home Automation”, Human Factors inPractice, December 1990, 1-36.

This generalized system is shown in FIG. 23, in which the sensor array2301 interfaces with a microprocessor 2302 with a serial data port2302a, which transmits sensor data to a control 2303. The control 2303further interfaces or includes a data pattern recognition system 2304and an interface and programming console 2305 according to the presentinvention, using the aforementioned intelligent features and adaptivepattern recognition techniques. The control 2203 controls the plant2306, which includes all the controlled actuators, etc.

EXAMPLE 9

Adaptive Graphic Interface

A “smart screen” aspect according to the present invention is furtherexplored in the present example. This aspect of the present inventionallows the interface to anticipate or predict the intent of the user, toprovide, as a default user choice, the most likely action to be taken bythe user of the programmable device as a default, which may be eitheraccepted or rejected by the user, without inordinate delay to the user.The intelligent selection feature may also automatically choose anoption and execute the selected option, without further intervention, incases where little or no harm will result. Examples of such harm includea loss of data, a substantial waste of the user's time and aninappropriate unauthorized allocation of computational resources.

When a user regularly applies the VCR device, for example, to record aparticular television show which appears weekly on a given televisionchannel, at a given time, on a given channel, such an action could beimmediately presented to the user as a first option, without forcing himto explicitly program the entire sequence. Likewise, if the user hasalready entered such a command, the presented choices could include asecond most likely selection, as well as the possibility of cancelingthe previously entered command.

Further, if an entire television programming guide for a week or monthis available as a database, the interface could actively determinewhether the desired show is preempted, a repeat (e.g., one which hasbeen previously recorded by the system), changed in time or programmingslot, etc. Thus, the interface could present information to the user, ofwhich he might not be aware, and/or predict an action based on thatinformation. Such a device could, if set in a mode of operation thatallows such, automatically execute a sequence of instructions based on apredicted course of action. Thus, if a user is to be absent for aperiod, he could set the machine to automatically record a show, even ifthe recording parameters are not known with precision at the time ofsetting by the user. Of course, this particular embodiment depends onthe availability of a database of current broadcast schedules, however,such a database may generally be available, e.g., in an on-linedatabase.

Such an on-line database system of known type may be used and need notbe described in detail herein. Alternately, a printed schedule ofbroadcasts may be scanned into a computer and the printed informationdeciphered (e.g., OCR) to gain access to a database. Other methods mayalso be used to access scheduling information, e.g. access channels oncable systems, as well as other broadcast information identifying futureand imminent programming. Together, these methods allow semiautonomousoperation, guided by programming preferences rather than explicitprograms, where such explicit instruction is absent.

The smart screens according to the present invention may be implementedas follows. The controller may be, for example, an Apple Power Macintosh8100/110 AV computer, operating under Macintosh 7.5 operating system.The Hypercard™ 2.3 software may be used to implement the screeninterface, which incorporates the above-described features, which isgenerally compatible with the Hyperpad software described above.HyperCard^(TM) is mentioned due to its capabilities to referenceexternal programs, thus allowing interfacing to various software andhardware devices. A more global scripting language, such as Frontier byUserLand Software Inc., may also be used, especially where low levelhardware control of interfaced devices, such as a VCR, multimediaadapter, or the like is desired. Apple Applescript may also be used. TheQuicktime format may be used to store and recall data, however, manyacceptable formats exist. The input device is an Apple Desktop Bus (ADB)mouse (Apple Computer Inc., Cupertino, Calif.), and the output displayis an 8 bit or 24 bit graphics color adapter connected to, e.g., a 14″color monitor. In addition, various parameters concerning the use of theinterface are stored in the computer's memory, and a non-volatile massstorage device, such as a hard disk drive, or Electrically ErasableProgrammable read Only Memory (EEPROM) or Erasable Programmable ReadOnly Memory (EPROM), as well as battery backed Random Access Memmory(RAM) could also be used.

A more modern implementation might employ, for example, a single or dualPentium II 450 MHz workstation, running Microsoft Windows NT 4.0 (orWindows 2000, when available).

The hardware is a matter of choice, including memory, monitor, pointingdevice, graphic display card, video capture card, mass storage options,and the like. Preferably, a hardware codec is provided, for example aMedia 100, Inc. Broadway device. The software may be, for example,Microsoft Visual Basic 5.0 or other suitable development language.

Intel Pentium-based platforms may also be used, preferably in IBM-PCcompatible implementations. Intel 80860 and/or Intel 80960 processorplatforms may also be used.

Alternatively, other Apple Power PC, Macintosh (MC680X0 series) or IBMPower PC implementation may be used, providing the advantage ofincreased processing power over Motorola 680X0 derivatives. The specificPower PC employed may be any version, including desktop system versionsavailable from Apple and IBM and embedded versions from IBM andMotorola. These Power PC processors may also be provided in a parallelprocessing implementation. Further, custom implementations of Power PChardware optimized for the relevant computational tasks may be employed.

Of course, other systems, including DEC Alpha and HP 9000 systems mayalso be employed, as well as SPARC, MIPS, and other available RISCsystems. While RISC systems, possibly supplemented with DSP hardware,are presently preferred because of their efficiency in executing thepattern recognition tasks, Complex Instruction Set Computer (CISC).,hybrid and other known processing systems may be employed. The TexasInstruments TMS320C80 combines a Reduced Instruction Set Computer (RISC)processor, Arithmetic logoc Unit (ALU) and four DSP processors on asingle chip, and is therefore a preferred processor for implementingvarious aspects of the system, especially mathematical processingincluding DCT and correlations.

According to the present invention, the interface may performcomparatively simple tasks, such as standard graphic user interfaceimplementation with optimized presentation of screen options, or includemore complex functionality, such as pattern recognition, patternmatching and complex user preference correlations. Therefore, hardwarerequirements will range from basic 68040, 80486, Pentium, Power PC,MIPS, SPARC, Digial Equipment Corp. (DEC, now Compaq Computer Corp.)Alpha, or other microprocessors which are used to perform visual oraudio interface functions, to much special purpose processors forimplementation of complex algorithms, including mathematical, neuralnetwork, fuzzy logic, and iterated function systems (fractals).

It should be noted that, while many aspects of the intelligent interfaceaccording to the present invention do not require extremely high levelsof processing power, and therefore may be provided with inexpensive andcommonly available computing hardware, other aspects involve complexpattern recognition and advantageously employ powerful processors toachieve a short processing latency. Both simple and complex interfacesystems, however, are included within the scope of the presentinvention. Processing may be distributed in different fashions, so thatcomplex functionality may be implemented with relatively simple localhardware, with a substantial amount of required processing for a highlevel of functionality performed centrally, and for a large number ofusers.

From the stored information regarding the prior use of the interface bythe user, including prior sessions and the immediate session, and acurrent state of the machine (including a received data stream andinformation relating to the data stream previously stored), a predictedcourse of action or operation may be realized. This predicted operationis, in the context of the current user interface state, the mostprobable next action to be taken by the user.

The predicted operation is based on: the identity of the user, if morethan one user operates the interface and machine, the informationalready entered into the interface during the present programmingsession, the presently available choices for data entry, settings forthe use of the machine, which may be present as a result of a “setup”operation, settings saved during a prior session, and a database ofprogramming choices. In the case of a HyperCard script, the interfacesoftware calls another program which has access to the necessary data inthe memory, as well as access to any remote database which may benecessary for implementation of the function. Using a predictivetechnology, such as Boolean logic, fuzzy logic, neural network logic, orother type of artificial intelligence, a most probable choice may bepresented to the user for his approval, or another alternative choicemay be selected. Further, a number of most probable choices may bepresented simultaneously or in sequence, in order to improve theprobability that the user will be immediately or quickly presented withan acceptable choice. If multiple choices are presented, and there islimited room on the display, two (or more) similar choices may be mergedinto a single menu selection, which may be resolved in a secondary menuscreen, e.g. a submenu or dialog box.

FIG. 24 shows a system for correlating a user's preferences with aprospective or real-time occurrence of an event. The input device 2401,which is a remote control with a pointing device, such as a trackball,provides the user's input to the control 2402. The program is stored ina program memory 2403, after it is entered. The control 2402 controls aplant 2404, which is a VCR. The control also controls an on-screenprogramming interface 2405, through which the user interactively entersthe program information. Each program entry of the user is submitted tothe user history database and preferences module 2406, which may alsoreceive explicit preference information, input by the user through theinput device 2401. The prospective and real time event characterizationunit 2407 uses any and/or all relevant information available in order todetermine the character of a signal input, which is a video signal, fromthe signal receiver 2408. A signal analyzer 2409 provides a preliminaryanalysis and characterization of the signal, which is input to theprospective and real time event characterization unit 2407. Theprospective and real time event characterization unit 2407 alsointeracts and receives an input from a telecommunication module 2410,which in turn interacts and receives information from an on-linedatabase 2411. A user preference and event correlator 2412 produces anoutput relating to a relatedness of an event or prospective event and auser preference. In the event of a high correlation or relatedness, thecontrol 2402 determines that the event or prospective event is a likelyor most likely predicted action. The prospective event discussed aboverefers to a scheduled event, which is likely to occur in the future. Thecharacterization unit also has a local database 2413 for storingschedule information and the like.

In the particular context of a videotape, one consideration of the useris the amount of time remaining on the tape. Generally, users wish tooptimally fill a tape without splitting a program, although theoptimization and non-splitting parameters may vary between users.Therefore, the length of the tape and the amount and character of otheritems on the tape are also factors to be employed in determining a mostdesired result. With respect to this issue, the interface may maintain alibrary function which allows the identification of a partially filledtape for recording under given circumstances. The interface may alsooptimize a playback by selecting a tape containing a desired sequence ofmaterials.

The intelligent interface may also be used as a part of an educationalsystem, due to its ability to adapt to the level of the user anddynamically alter an information presentation based on the “user level”,i.e. the training status of the user, and its ability to determine areasof high and low performance. Likewise, the intelligent interfaceaccording to the present invention may also be used in a businessenvironment for use by trained individuals who require relatively staticsoftware interface design for consistence and “touch typing” withmemorized keystroke or mouse click sequences. In this case, theintelligent functionality is segregated into a separate user interfacestructure, such as an additional “pull down menu” or other availablescreen location. While the interface always monitors user performance,the impact of the analysis of the user is selectively applied. Useranalysis may also be used for performance evaluation according to anobjective criteria, based on continuous monitoring. In a networkenvironment, user profile and evaluation may be made portable, stored soas to be accessible from any networked device the user may interactwith, from office computers to thermostats to photocopying machines tocoffee machines.

EXAMPLE 10

Intelligent Adaptive VCR Interface

A user interacting with the device intends to record a particularprogram, “Married With Children” (Fox, Sunday, 9:00 p.m., etc.) on itsever occurrence. This intent, however, is to provide a full library ofepisodes, and not to duplicate episodes. The particular program issubject to the occurrence of reruns, syndicated distribution, timeshifting of performance, preview scenes and advertisements. Further,various actors appearing in the particular program also appear in othercapacities and roles on television. Therefore, after this intent iselucidated, the interface scans available directories of programming todetermine when “Married With Children” will be broadcast. In addition,to the extent possible, all channels may be monitored, in the event thatthe directories or erroneous or incomplete.

It is noted that the interface may be quite effective if it is used fora number of applications, such as television, radio, desktop computer,and even kitchen and HVAC system. For example, preferences forprocessing MTV or other music video information may be directly relevantto processing of radio or other music reproduction devices, and viceversa.

At some point in the process, preferably prior to substantiveprogramming input, the interface performs a self-diagnostic check todetermine whether the machine is set up and operating correctly. Thiswould include a determination of whether the clock has been set andthereafter operating continuously. Of course, the clock could have, inpractice, a battery to minimize the occurrence of problems relating toclock function. The interface would then, if the clock is not properlyset, and if there is no telecommunication or other external means forautomatically determining the exact time, present the user with a menuselection to set the proper time. Of course, if the correct time isavailable to the apparatus in some form, this could be automaticallyobtained, and the internal clock updated, without intervention. Thesesame sources may be used to verify the accuracy of an internal clock.Further, if a reliable external clock system is available, an internalclock may be dispensed with or ignored. Time may also be inferred basedon the regular schedules of broadcasts, e.g., the 11:00 p.m. news beginsat 11:00 p.m. If the user does not have access to a source of the exacttime, the step of correcting the time may be deferred, although at somepoint the user should be reminded to verify the clock information. Theuser may thus be able to override a machine-generated request or attemptto correct the time data.

If the machine has access to an external source of the exact time, itwould then preferably access this source first. Such sources of exacttime include a telephone connection to a voice line which repeats thetime. The computer would then perform a speech recognition algorithmwhich would be used to determine the time. Such a speech recognitionalgorithm could also be used as a part of the user interface for otherpurposes, i.e. a speech recognition system is not supplied solely forobtaining time information. Alternatively, a modem or communicationdevice could be used to obtain the time in digitally coded form over anetwork, which would alleviate the need for speech recognitioncapabilities for this function. An on-line connection could also be usedin order to obtain information concerning television scheduling.

A further method for obtaining accurate time information is to access avideo signal which contains the desired time information. For example,many cable broadcasting systems have a channel which continuouslybroadcasts the time in image form. The interface tunes this channel, andacquires a representation of the screen image, thereafter performing acharacter recognition algorithm to capture the time information. Thischaracter recognition algorithm could also be used to obtain or captureinformation regarding programming schedules, stock prices, and othertext information which may appear on certain cable broadcast channels.

Thus, the interface, in obtaining necessary information, employs suchavailable data source access methods as speech recognition, characterrecognition, digital telecommunication means, radio wave reception andinterpretation, and links to other devices.

In interacting with the apparatus, the user first identifieshimself/herself to the machine, which can occur in a number of ways.This step may be dispensed with, or at least trivialized, if only oneuser regularly interacts with the apparatus. Otherwise, suchidentification may be important in order to maintain the integrity ofthe user profiles and predictive aspects of the interface. An radiofrequency transponder (RF-ID), infrared transponder (IR-ID) system mayautomatically determine the user based on a devices, which may beconcealed in a piece of jewelry or wristwatch. The user may also beidentified by voice pattern recognition, speaker independent voicerecognition, video pattern recognition, fingerprint, retinal scan, orother biometric evaluation. An explicit entry of the user identity mayalso be employed, wherein the user types his/her name on a keyboard orselects the name or unique identifier from a “pick-list”. The interface,upon identifying the user, retrieves information regarding the user,which may include past history of use, user preferences, usersophistication, patterns of variation of user, which may be based on,e.g., time, mood, weather, lighting, biometric factor or other factors.

Thus, after completing system diagnostics, including the time-checkfunction referred to above, the system next determines or predicts thedesired function of the user. In this regard, if more than one user hasaccess to the system, the user identifies himself to the interface, in auser identification step 1701 or an analogous action, which may be acoded entry, or a selection from the menu. If the interface has voicerecognition capability, then the user may be recognized by his voicepattern, or merely by stating his name. The interface then accesses thememory for a profile of the past use of the machine by the user, whichmay include the entire prior history, relevant abstracts of the history,or derived user preferences, as shown in the personalized startup basedon user profile step 1702, which information is also stored and used inthe past user history determining element 2107. These choices differ inthe amount of storage necessary in order to retain the desiredinformation.

Thus, if the user has only used the VCR to record, e.g., the NationalBroadcasting Company (NBC) 11 o'clock news, i.e., record all days from11:00 p.m. to 11:30 p.m. on NBC, in the past, the most likely currentpredicted choice would be the NBC 11 o'clock news. If the interface wereto present a number of choices, having lower probability, then itinterprets the recording history to be “news” based on a database ofbroadcast information. Therefore, a prediction of lower probabilitywould be American Broadcasting Company (ABC) or Central BroadcastingCompany (CBS) news at, e.g., 11:00 p.m., and the NBC news at, e.g., 5:00p.m. In a cable television system, there may be a number of NBCaffiliated news alternatives, so that these alternatives may beinvestigated first before other networks or the like are presented aslikely choices. In addition, where a video feed is unavailable, a textfeed from the internet or an on-line service may be acquired as aprobable alternative.

Thus, a number of likely choices, based on intelligently determinedalternatives, as well as adaptation based on determined userpreferences, are initially presented to the user, along with a menuselection to allow rejection of these predicted choices. In this case,the user selects the “reject” selection, and the system presents theuser with a next predicted desired menu choice. Since the user history,in this case, does not provide for another choice of particularly highprobability, the user is prompted to explicitly choose the programsequence by day, time, channel, and duration. The user then enters thestarting time for recording according to the methods described above.The interface then searches its databases regarding the user andbroadcast listings to present a most likely choice given that parameter,as well as all available alternatives. In this case, the user history isof little help, and is not useful for making a prediction. In othercases, the system uses its intelligence to “fill in the blanks”, whichcould, of course, be rejected by the user if these are inaccurate orinappropriate. The most likely choices are then those programs thatbegin at the selected time. If the user had input the channel ornetwork, instead of starting time, then the presented choices would bethe broadcast schedule of the channel, e.g. channel 5 or Fox, for theselected day.

The user then selects one of the available choices, which completes theprogramming sequence. If no database of broadcasts is available, thenthe user explicitly defines all parameters of the broadcast. When theprogramming is completed, the interface then updates its user database,prompts the user to set the VCR to record, by, e.g., inserting a blankor recordable tape.

If the predicted desire of the user is of no help, or the user seeks toexplicitly program the system, a manual program entry system isavailable. Where there is no useful prediction of the user, theinterface may request a training session, which may be a generalinquiry, or specifically directed to immediately forthcoming broadcasts,or both.

In this case, after a failure to predict a desired program, the userthen proceeds to explicitly program the VCR interface to record “Marriedwith Children” on Fox at 9:00 p.m. on Sunday evening. If a database isavailable, it might also show that “Married with Children” is alsosyndicated in re-runs, and therefore various episodes may be availableon other channels at other times. Thus, during the subsequent session,both the premier showing and re-run of “Married With Children” would beavailable predicted choices, along with the 11 o'clock News on NBC.

The user having demonstrated a preference for “Married with Children”,the interface then characterizes the program. This includes, forexample, a characterization of the soundtrack, the background,foreground, actors and actresses present, credits, etc. The interfacethen attempts to correlate the features present in the referenceselection with other available selections. This comparison may be with apreformed database, providing immediate results, or prospectively, afterentry of the reference selection. Of course, a number of correlationfunctions may proceed simultaneously, and various choices may be mergedto form a compound reference selection, any ambiguity in which to belater resolved. Further, as various “episodes” of the referenceselection occur, the system appends and integrates the most recentoccurrence with the stored reference information, thus updating thereference database.

When an occurrence is identified, it is immediately buffered, until suchtime as the particular episode may be compared against previously storedepisodes. If two identical broadcasts occur simultaneously, one may beselected, i.e., the one with the best reception. When the episode isidentified, if it is new, the buffered broadcast information ispermanently stored; if it is previously stored, the buffer is flushedand the occurrence is further ignored as a “hit”. Since the apparatus isnow not responding to a direct request, it may then perform varioushousekeeping functions, including updating databases of broadcasts andthe like. This is because, although the apparatus is preferably highlytrained upon manufacture, a large number of new broadcasts are alwaysbeing created and presented, so that the apparatus must constantlymaintain its “awareness” of data types and trends, as well as update itspredicted preferences of the user(s).

Based on input from the user, other programming including the sameactors and/or actresses may be processed, e.g., recorded. For example,Katey Segal periodically appears on “Jay Leno” as a musical guest, andtherefore may be recorded in these appearances.

EXAMPLE 11

Intelligent Adaptive VCR Interface

Another example of the use of the present programming system allows ahybrid request which does not correspond to any single broadcastschedule entry. In this case, if the user instead wishes to recordweather reports on all channels, the interface may be of further help.The interface controls a plurality of tuner elements 2502 of a videosignal reception device 2501, so that a plurality of broadcasts may besimultaneously received. Using the mass storage and possibly image datacompression described above, a plurality of broadcasts may also berecorded simultaneously in the intermediate storage 2503. The massstorage may be multiple VCRs, optical storage, magnetooptical storage,magnetic storage including disk (e.g. single disks, multimediacompatible disks, RAID, etc.) tape (QIC, 8 mm, 4 mm, etc.). Preferably,the archival recording medium is recordable DVD or possibly recordableCD-ROM.

The optical recording tape produced by ICI, Inc., or other card or tapeoptical storage medium might also be a useful storage medium for largevolumes of data, as might be generated by recording multiple videosignals. The known implementations of the ICI product system best suitedfor commercial or industrial use and not for individual consumer use.

In any case, the interface 2506 accesses its associated database 2413 todetermine, at a given time, which channels are broadcasting “news”. Theinterface system might also randomly or systematically monitor or scanall or a portion of the available broadcasts for “special reports”. Theinterface system then monitors these channels for indicia of a “weather”information content broadcast. For example, the newscaster who appearsto report the weather on a given show is usually the same, so that apattern recognition system 2505 of the video frame could indicate thepresence of that newscaster. In addition, the satellite photographs,weather radar, computer generated weather forecast screens, etc. areoften similar for each broadcast. Finally, news segments, such as“weather” often appear at the same relative time in the broadcast. Usingthis information, the interface system selects certain broadcastsegments for retention.

This retention begins at a beginning of a news segment, such as“weather”, stop recording during commercials, and continues after returnfrom break, on all selected channels. In order to assist in makingaccurate decisions, the monitored broadcasts may be stored in atemporary storage medium until a decision is made, and thereaftertransfer the recording to a more permanent storage medium if that beappropriate. It is noted that the system of the present invention isintelligent, and may therefore “learn” either explicitly, or throughtraining by example. Therefore, if the system made an error during theprocess, the user may define the error of the system, e.g., a substitutenewscaster or rearrangement of news segments, so that the interfacesystem has a reduced likelihood of making the same error again. Thus,while such a system is inherently complex, it poses significant useradvantages. Further, while the interface system itself is sophisticated,it provides simplicity, with inductive reasoning and deductive reasoningfor the user.

Thus, a minimum of user interaction is required even for complex tasks,and nearly full automation is possible, as long as the user andapparatus are able to communicate to convey a preference. As a furtherembodiment according to the present invention, the interface system willstored transmitted data, and subsequently review that data, extractingpertinent information. The stored data may then be deleted from thestorage medium. In this regard, the system may be self learning,.

It is noted that various algorithms and formulae for patternrecognition, correlation, data compression, transforms, etc., are knownto those skilled in the art, and are available in compendiums, such asNetravali, Arun N., and Haskell, Barry G., “Digital PicturesRepresentation and Compression”, Plenum Press, New York (1988); Baxes,Gregory A., “Digital Signal Processing, A Practical Primer”,Prentice-Hall, Englewood Cliffs, N.J. (1984); Gonzalez, Rafael C.,“Digital Image Processing”, Addison-Wesley, Reading, Mass. (1987), and,of a more general nature, Press, William H. et al, “Numerical Recipes inC The Art of Scientific Computing”, Cambridge University Press, 1988.

EXAMPLE 12

Intelligent Adaptive VCR Interface

A further example of the use of the advanced intelligent features of thepresent invention is the use of the system to record, e.g., “live”musical performances. These occur on many “talk” shows, such as “TonightShow” (NBC, 11:30 p.m. to 12:30 p.m., weeknights), “Saturday Night Live”(NBC 11:30 p.m. to 1:00 a.m. Saturday-Sunday), and other shows or“specials” such as the “Grammy Awards”. The interface, if requested bythe user to record such performances, then seeks to determine theiroccurrence by, e.g., analyzing a broadcast schedule; interacting withthe on-line database 2411; and by reference to the local database 2413.When the interface determines with high probability that a broadcastwill occur, it then monitors the channel(s) at the indicated time(s),through the plurality of tuners 2502. The system may also autonomouslyscan broadcasts for unexpected occurrences.

In the case of pay-per-view systems and the like, which incorporateencrypted signals, an encryption/decryption unit 2509 is provided fordecrypting the transmitted signal for analysis and viewing. This unitalso preferably allows encryption of material in other modes ofoperation, although known decryption systems without this feature mayalso be employed with the present system. During the monitoring, theinterface system acquires the audio and video information beingbroadcast, through the signal receiver 2408, and correlates thisinformation with a known profile of a “live musical performance”, in thepreference and event correlator 2412. This must be distinguished frommusic as a part of, e.g., a soundtrack, as well as “musicals” which arepart of movies and recorded operas, if these are not desired by theuser. Further, music videos may also be undesirable. When thecorrelation is high between the broadcast and a reference profile of a“live musical performance”, the system selects the broadcast forretention. In this case, the information in the intermediate storage2503 is transferred to the plant 2507, which includes a permanentstorage device 2508. The intermediate storage 2503 medium is used torecord a “buffer” segment, so that none of the broadcast is lost whilethe system determines the nature of the broadcast. This, of course,allows an extended period for the determination of the type ofbroadcast, so that, while real-time recognition is preferred, it is notabsolutely necessary in order to gain the advantages of the presentinvention. The buffer storage data, if not deleted, also allows a userto select a portion for retention that the interface system hasrejected.

Thus, while it is preferable to make a determination in real time, or atleast maintain real time throughput with a processing latency, it ispossible to make an ex post facto determination of the nature of thebroadcast program. By using an available delay, e.g., about 5 to about300 seconds, or longer, the reliability of the determination can begreatly increased as compared to an analysis of a few frames of videodata, e.g., about 15 to about 300 mS. An intermediate reliability willbe obtained with a delay of between about 300 to about 5000 mS. Asstated above, the storage system for this determination need not beuncompressed nor lossless, so long as features necessary to determinethe character of the broadcast are present. However, it is preferredthat for broadcast recording intended for later viewing, the storage beas accurate as possible, so that if a compression algorithm isimplemented, it be as lossless as reasonable given the variousconstraints. The MPEG-2 standard would be applicable for this purpose,though other video compression systems are available.

In a preferred situation, approximately 5 minutes of broadcast materialis analyzed in order to make a determination of the content. Thisbroadcast material is stored in two media. First, it is stored in aformat acceptable for viewing, such as video tape in a videotaperecorder, or in digital video format, e.g., uncompressed, MPEG-2.Second, it is received in parallel by the computer control, where thedata is subject to a number of recognition and characterizationprocesses. These are performed in parallel and in series, to produce astored extracted feature matrix. This matrix may contain any type ofinformation related to the broadcast material, including an uncompressedsignal, a compressed signal, a highly processed signal relating toinformation contained in particular frames and abstract features,spatially and temporally dissociated from the broadcast signal, yetincluding features included in the broadcast which relate to the contentof the broadcast.

One possible method incorporates one or more digital signal processorbased coprocessor elements, which may be present on, e.g., Nubus cardsin the Macintosh Quadra 950, Apple Power PC, PCI card in Pentium-basedMS-DOS/Windows 3.1, 3.11, 95, 98, NT computers (or Macintosh PCI-basedcomputers), other Power PC based computers. These elements may be basedon C-Cube CL550 (JPEG compression), Analog Devices ADSP-21020, AnalogDevices ADSP-21060, AT&T (formerly American Telephone and Telegraph Co.)DSP32C, AT&T DSP3210, AMD 29000 series, Motorola DSP 96000ADS, TexasInstruments TMS 320C40, TMS 320C80, IBM Mwave, or other known devices.Other devices are also available from Analog Devices, AT&T, DSP Group,Motorola, NEC, SGS-Thomson, Sharp, Texas Instruments, Zilog, Zoran, andother vendors. See, EDN, May 11, 1995, pp. 40-106; Bursky, D., “ImprovedDSP ICs Eye New Horizons”, Electronic Design, Nov. 11, 1993, pp. 69-82.DSP systems, which generally have an architecture optimized for theefficient and rapid execution of repetitive numeric calculations, aredesirable for certain pattern recognition tasks, and may be provided asa tightly coupled parallel processing array to increase throughput.

A known board containing a DSP is the MacDSP3210 by Spectral InnovationsInc., containing an AT&T digital signal processor and an MC68020 CISCprocessor, and which uses the Apple Real-time Operating System Executive(A/ROSE) and Visible Cache Operating System (VCOS). It is preferred thatthe processors employed be optimized for image processing, because oftheir higher throughput in the present image processing applications, toprocess the video signals, and more other signal processors to analyzethe audio signals. Of course, general purpose processors may be used toperform all calculations. An array processor which may be interfacedwith a Macintosh is the Superserver-C available from Pacific ParallelResearch Inc., incorporating parallel Inmos Transputers. Such an arrayprocessor may be suitable for parallel analysis of the image segment andclassification of its attributes.

Pattern recognition processing, especially after preprocessing of thedata signal by digital signal processors and image compression engines,may also be assisted by logical inference engines, such as FUTURE (FuzzyInformation Processing Turbo Engine) by The Laboratory for InternationalFuzzy Engineering (LIFE), which incorporates multiple Fuzzy SetProcessors (FSP), which are single-instruction, multiple data path(SIMD) processors. Using a fuzzy logic paradigm, the processing systemmay provide a best fit output to a set of inputs more efficiently thanstandard computational techniques, and since the presently desiredresult requires a “best guess”, rather than a very accuratedetermination, the present interface is an appropriate application ofthis technology.

As noted above, these processors may also serve other functions such asvoice recognition for the interface, or extracting text from videotransmissions and interpreting it. It is also noted that, while some ofthese coprocessing engines are now costly, these costs are decreasingand the present invention therefore includes the use of sophisticatedpresent designs as well as future devices which may be used to performthe stated functions. The continued development of optical computers mayalso dramatically reduce the cost of implementing this aspect of thepresent invention; however, the present state of the art allows thebasic functions to be performed. See attached appendix of references,incorporated herein by reference, detailing various optical computingdesigns.

A real time operating system may be employed, of which there are anumber of available examples. Some older examples include SPOX DSPoperating system, IBM's Mwave operating system and AT&T's VCOS operatingsystem. These operating systems, and possibly others, are to besupported by Microsoft Inc.'s Windows 95 operating system ResourceManager function.

It is noted that various methods are available for determining arelatedness of two sets of data, such as an image or a representation ofan image. These include the determination of Hausdorff distance, fuzzycorrelation, arithmetic correlation, mean square error, neural network“energy” minimization, covariance, cross correlation, and other knownmethods, which may be applied to the raw data or after a transformationprocess, such as an Affine transformation, a Fourier transformation, aGabor transformation, a warping transformation, a color maptransformation, and the like. Further, it is emphasized that, in imageor pattern recognition systems, there is no need that the entire imagebe correlated or even analyzed, nor that any correlation be based on theentirety of that image analyzed. Further, it is advantageous to allowredundancy, so that it is not necessary to have unique designations forthe various aspects of the data to be recognized, nor the patterns to beidentified as matching the uncharacterized input data.

The MSHELL from Applied Coherent Technology is a software system thatruns on a Mercury MC3200 array processor, in conjunction with a DataTranslation DT2861 or DT2862. The NDS1000 Development System fromNestor, Inc., provides image recognition software which runs on a PCcompatible computer and a Data Translation DT2878.

The C-Cube CL550 is disclosed in “C-Cube CL550 JPEG Image CompressionProcessor”, Preliminary Data Book, August 1991, and addendum dated Nov.20, 1991, and products incorporating the CL550 include the JPEG VideoDevelopment Kit (ISA bus card with Chips and Technologies PC video82C9001A Video Window Controller), and the C-Cube CL550 DevelopmentBoard/PC for ISA Bus (CL550, for use with Truevision TARGA-16 or ATVistacards) or for NuBus (Macintosh). The so-called C-Cube “CL950” is a MPEGdecoder device. Such a device as the CL950 may be particularly usefulfor use in the present VCR for reproducing compressed program material,which may be compressed by the present apparatus, or may be used fordecompressing pre-compressed program material. Other MPEG-1 and MPEG-2encoding and decoding devices are known.

It is noted that all functions of a VCR would also be facilitated by theuse of such powerful processors, and thus it is not only these advancedfunctions which are enabled by these advanced processors andcoprocessors. It is also noted that these image recognition functionsneed not necessarily all be executed local to the user, and may in factbe centralized with resultant processed data transmitted to the remoteuser. This would be advantageous for two reasons: first, the user neednot have an entire system of hardware localized in the VCR, and second,many of the operations which must be performed are common to a number ofusers, so that there is a net efficiency to be gained.

EXAMPLE 13

Intelligent Adaptive VCR Interface

The interface of the present invention incorporates an intelligent userinterface level determination. This function analyzes the quality of theuser input, rather than it's content. Thus, this differs from the normalinterface user level determination which requires an explicit entry ofthe desired user level, which is maintained throughout the interfaceuntil explicitly changed. The present interface may incorporate the“smart screen” feature discussed above, which may, through its analysisof the past user interaction with the interface predict the most likelypredicted user input function. Thus, the predictive aspects of thepresent invention may be considered a related concept to the intelligentuser level interface of the present invention. However, the followingbetter serves to define this aspect of the invention.

The input device, in addition to defining a desired command, alsoprovides certain information about the user which has heretofore beengenerally ignored or intentionally removed. With respect to atwo-dimensional input device, such as a mouse, trackball, joystick,etc., this information includes a velocity component, an efficiency ofinput, an accuracy of input, an interruption of input, and a highfrequency component of input. This system is shown schematically in FIG.21, which has a speed detector 2104, a path optimization detector 2105,a selection quality detector 2106, a current programming status 2108, anerror counter 2109, a cancel counter 2110, a high frequency signalcomponent detector 2112, an accuracy detector 2113 and a physio-dynamicoptimization detector 2114. In addition, FIG. 21 also shows that theinterface also uses a past user history 2107, an explicit user levelchoice 2111 and an explicit help request 2115.

This list is not exclusive, and is somewhat dependent on thecharacteristics of the specific input device. For a mouse, trackball, orother like device, the velocity or speed component refers to the speedof movement of the sensing element, i.e. the rotating ball. This mayalso be direction sensitive, i.e., velocity vector. It is inferred that,all other things being equal, the higher the velocity, the more likelythat the user “knows” what he is doing.

The efficiency of input refers to two aspects of the user interface.First, it refers to the selection of that choice which most simply leadsto the selection of the desired selection. For example, if “noon” is anavailable choice along with direct entry of numbers, then the selectionof “noon” instead of “12:00 p.m.” would be more efficient. The secondaspect of efficiency has to do with the path taken by the user in movinga graphic user interface cursor or input device from a current positionto a desired position. For example, a random curve or swiggle betweenlocations is less efficient than a straight line. This effect islimited, and must be analyzed in conjunction with the amount of time ittakes to move from one location of a cursor on the screen to another; ifthe speed of movement is very rapid, i.e. less than about 400 mS for afull screen length movement, or less than about 300 mS for smallmovements, then an inefficiency in path is likely due to the momentum ofthe mouse and hand, momentum of the rolling ball, or a physiological arcof a joint. This aspect is detected by the physio-dynamic optimizationdetector 2114. Thus, only if the movement is slow, deliberate, andinefficient, should this factor weigh heavily. It is noted that arcs ofmovement, as well as uncritical damping of movement around the terminalposition may be more efficient, and a straight path actuallyinefficient, so that the interface may therefore calculate efficiencybased on a complex determination, and act accordingly where indicated.

Thus, an “efficient” movement would indicate an user who may work at ahigh level, and conversely, an inefficient movement would indicate auser who should be presented with simpler choices. The efficiency ofmovement is distinguished from gestures and path dependent inputs, suchas drawing and painting. These may be distinguished based on machinestatus or context. Further, the interface may recognize gestures in manycontexts. Therefore, gesticulations must be distinguished from commandinputs before further processing. Gesticulations, like path efficiency,may also be analyzed separately from the basic command input, andtherefore may be provided as a separate input stream on an interfacelevel rather than an application level, thus allowing cross applicationoperation.

Likewise, if a movement is abrupt or interrupted, yet follows anefficient path, this would indicate a probable need for a lower userinterface level. This would be detected in a number of elements shown inFIG. 21, the speed detector 2104, a high frequency signal componentdetector 2112, an accuracy detector 2113 and a physio-dynamicoptimization detector 2114. In addition, FIG. 21 also shows the use of apast user history 2107, an explicit user level choice 2111 and anexplicit help request 2115.

While the interface may incorporate screen buttons which are smart, i.e.those which intelligently resolve ambiguous end locations, the accuracyof the endpoint is another factor in determining the probable level ofthe user. Thus, for example, if a 14″ color monitor screen is used,having a resolution of 640 by 480 pixels, an accurate endpoint locationwould be within a central area of a screen button of size about 0.3″ byabout 1.0″, would be an area of about 0.25″ by about 0.75″. A cursorlocation outside this location, but inside the screen button confineswould indicate an average user, while a cursor location outside thescreen button may be inferred to indicate the button, with an indicationthat the user is less experienced in using the pointing device.

Finally, in addition to the efficiency of the path of the cursorpointing device, a high frequency component may be extracted from thepointer signal by the high frequency signal component detector 2112,which would indicate a physical infirmity of the user (tremor), adistraction in using the interface, indecision in use, or environmentaldisturbance such as vibration. In this case, the presence of a largeamount of high frequency signal indicates that, at least, the cursormovement is likely to be inaccurate, and possibly that the user desiresa lower user level. While this is ambiguous based on the high frequencysignal content alone, in conjunction with the other indicia, it may beinterpreted. If, for example, the jitter is due to environmentalvibrations, and the user is actually a high level user, then theresponse of the user level adjust system would be to provide a screendisplay with a lowered required accuracy of cursor placement, withoutnecessarily qualitatively reducing the implied user level of thepresented choices, thus, it would have an impact on the displaysimplification 2103, with only the necessary changes in the current userlevel 2101.

Alternatively, the user may input a gesture, i.e., a stylized inputhaving no other command input meaning, which may be detected byanalyzing the input. The input may be a manual input, voice, image orthe like. A number of different gestures may be recognized. Thesegestures are generally explicit inputs, which allow a voluntary actionto be interpreted as input information to the interface.

EXAMPLE 14

Intelligent Telephone Device Interface

Likewise, the present interface could be used to control complextelecommunications functions of advanced telephone andtelecommunications equipment. In such a case, the user display interfacewould be a video display, or a flat panel display, such as an LCDdisplay. The interface would hierarchically present the availablechoices to the user, based on a probability of selection by the user.The input device would be, for example, a small track ball near thekeypad. Thus, simple telephone dialing would not be substantiallyimpeded, while complex functions, such as call diversion, automatedteledictation control, complex conferencing, calleridentification-database interaction, and videotel systems, could easilybe performed.

EXAMPLE 16

Character Recognition of Video

The present invention may incorporate character recognition from thevideo broadcast for automatic entry of this information. This is shownschematically in FIG. 24, with the inclusion of the videotext andcharacter recognition module 2414. This information is shown to betransmitted to the event characterization unit 2407, where the detectedinformation is correlated with the other available information. Thisinformation may also be returned to the control 2402. Examples of thetypes of information which would be recognized are titles of shows, castand crew from programming material, broadcast special alerts, time (fromdigital display on special access channels), stock prices from “tickertape” on special access channels, etc. Thus, this technology addsfunctionality to the interface. In addition, subtitled presentationscould be recognized and presented through a voice synthesizer, to avoidthe necessity of reading the subtitle. Further, foreign languagesubtitles could be translated into, e.g., English, and presented. In aparticular embodiment, certain game shows, such as “Wheel of Fortune”have alphanumeric data presented as a part of the programming. Thisalphanumeric text may be extracted from the image.

In a preferred embodiment, the character recognition is performed inknown manner on a buffer memory containing a frame of video, from adevice such as a Data Translation DT2851, DT2853, DT2855, DT2867,DT2861, DT2862 and DT2871. A contrast algorithm, run on, for example, aData Translation DT2858, DT2868, or DT2878, first removes thebackground, leaving the characters. This works especially well where thecharacters are of a single color, e.g. white, so that all other colorsare masked. After the “layer” containing the information to berecognized is masked, an algorithm similar to that used for opticalcharacter recognition (OCR) is employed. See, U.S. Pat. No. 5,262,860,incorporated herein by reference. These methods are well known in theart. This may be specially tuned to the resolution of the video device,e.g. NTSC, Super Video Home System (S-VHS), High Definition Televisionand/or Advannced Television System Committee (HDTV/ATSC-various includedformats), Improved definition television (IDTV), Enhanced DefinitionTelevision (EDTV), Multiple Sideband Encoding (MUSE), Phase AlternateLine (PAL), Sequential Coleur a Memoire (SECAM), MPEG-2 digital video,or other analog or digital transmission and/or storage formats, etc. Inaddition, since the text normally lasts for a period in excess of oneframe, a spatial-temporal image enhancement algorithm may be employed toimprove the quality of the information to be recognized, if it isindistinct in a single frame.

EXAMPLE 17

Smart House Interface

The present invention may also be incorporated into other types ofprogrammable controls, for example those necessary or otherwise used inthe control of a smart house. See, “The Smart House: Human Factors inHome Automation”, Human Factors in Practice, December 1990, 1-36. Theuser interface in such a system is very important, because it mustpresent the relevant data to the user for programming the control toperform the desired function. A smart house would likely have manyrarely used functions, so that both the data and the available programoptions must be presented in the simplest manner consistent with thegoal of allowing the user to make the desired program choice. Forexample, a smart house system with appropriate sensors might be used toexecute the program: “start dishwasher, if more than half full, at 9:00p.m.” This program might also include a program to load soap into thedishwasher or to check if soap is already loaded. A user who wishes todelay starting until 11:00 p.m. would be initially presented with thedefaults, including start time as an option, which would be simplymodified by correcting the starting time. The next time the same userwishes to program the device, an algorithm might change the predictedstarting time to, e.g. 10:00 p.m., which is a compromise between thehistorical choices. Alternatively, the new predicted start time might be11:00 p.m., the last actually programmed sequence. Finally, the nextpredicted start time might remain at 9:00 p.m. The resolution of thesechoices would depend on a number of factors: a preprogrammed expertsystem; any other prior history of the user, even with respect to otherappliances or in other situations; the context, meaning any othercontemporaneously programmed sequences; and an explicit input from theuser as to how the inputs should be evaluated for predictive purposes.

The expert system would balance factors, including disturbing noise fromthe dishwasher, which might be objectionable while persons are near thedishwasher, people are sleeping, or during formal entertainment. On theother hand, if the dishwasher is full, or its cleaned contents areneeded, the dishwasher should run. Some persons prefer to reshelvedishes in the evening, before sleep, so in those cases, the dishwashershould complete its cycle before bedtime. The dishwasher, on a hot watercycle, should not run during showers or baths, and preferably should notcompete with a clothes washer for hot water. The dishwasher preferablydoes not run during peak electrical demand times, especially ifelectrical rates are higher. Water conserving cycles should be selected,especially during droughts or water emergencies. If dishes remain in thedishwasher for an extended period, e.g., overnight, a moistening cyclemay be employed to help loosen dirt and to help prevent drying. Thus,the expert system is preprogrammed for a number of high levelconsiderations that might be common to a large number of users of thesystem, thus shortening the required training time of the system tolearn the preferences of the user. Such a sophisticated system mayeliminate the need entirely for adaptive responses, based on weighing ofconsiderations provided by the user. Of course, other considerations mayalso be included for the operation or delay of operation of thedishwasher. Further, these considerations are exemplary of the types ofconsiderations which might be employed in an expert system in a smarthouse.

The prior history of the user provides an excellent source ofinformation regarding the preferences of the user, although this issometimes not the most efficient means, and may often includecontradictory data. This historical use data is therefore analyzed in abroad context in order to extract trends, which over a number of usesmay be further extracted as “rules”. Often, the user history data willbe applied at a high level, and will interact with preexisting rules ofthe expert system, rather than to create new rules. In this case, theexpert system preferably includes a large number of “extra rules”, i.e.,those with an a priori low probability or low weighing, providing atemplate for future pattern matching. The past history may be evaluatedin a number of ways. First, an expert system may be used to analyze thepast usage pattern. Second, a neural network may be trained using thehistorical data along with any corrective feedback. Third, thehistorical data may be used to alter fuzzy logic rules orclassifications, either by expert system, neural network, or by otherknown means.

The context of use may also be used to determine a desired or predictedaction. Therefore, if on a single occasion, a number of changes aremade, for example during a large house party, the standard predictionswould not be altered, and thus a normal program would remain in effect.Of course, a new “house party” sequence would then be recognized andincluded as a new type of sequence for future evaluation. For example, ahouse party sequence might encompass a number of house systems. Thus,the delay of dishwasher until 11:00 p.m. allows all dishes from theparty to be placed in the dishwasher before starting. An alarm systemwould be generally deactivated, although various zones may be providedwith different protection; e.g., a master suite may be off-limits, withan alarm transmitting a signal to a user's beeper, rather than a call topolice or alarm service company. During the summer, the air conditionermight run even if doors and windows are open, even if the normal programprompts for door closings before the air conditioner is turned on.Likewise, exterior lighting would be turned on at dusk, with bug lightsturned on during the entire party. The user might individually make suchdecisions, which would be recognized as a group due to their proximityin time, or delineate the actions as a group. Thereafter, where some ofthese choices are made, and the profile of choices matches a “party”style, the remainder of the choices may be presented as a most likely orpredicted choice. The group of choices together might also be selectedfrom a menu of choices.

Context also relates to sensor data, which might include sensors inparticular appliances or unrelated sensors. For example, infrared motiondetectors may be used to estimate the number of persons present in ahouse. Likewise, heavy use of a bathroom, as detected by flushes,frequent light transitions or door openings, might also be useful asdata to estimate a crowd size. Temperature sensors, video imagingsensors, perimeter sensors, electrical sensors relating to the status ofappliances and machinery, and other types of sensors may provide datafor context determination.

Of course, explicit inputs must also be accommodated, which may beatomic instructions or complex combinations of instructions which maycontrol a single house system or a number of house systemssimultaneously. The explicit input preferably comes by way of theadaptive interface described throughout the present application, or aninterface incorporating particular aspects thereof.

The smart house system also controls the climate control system. Thus,it could coordinate temperatures, air flow and other factors, based onlearned complex behaviors, such as individual movement within thedwelling. Since the goal of the programming of the smart house is notbased on the storage of discrete information, but rather the executionof control sequences at various times and under certain circumstances,the control would differ in various ways from that of a VCR. However,the user interface system, adaptive user level, help system, and thelike would be common to both types of system. This differs from theFuzzy Logic controlled air conditioner available (in Japan) fromMitsubishi in that these prior art devices do not have an intelligentinterface of the present invention. It should also be noted that thecontrol for the VCR could be the same control as that for the smarthouse, so that the common elements are not redundant. Therefore, byapplying a single control to many tasks, a common user interface isused, and the cost is reduced.

EXAMPLE 18

Programmable Environmental Controller

The present Example relates to a programmable environmental controllerapplication. In this case, a sensor or sensor array is arranged todetect a change in the environment which is related to a climaticcondition, such as an open door. On the occurrence of the door opening,the system would apply a pattern recognition analysis to recognize thisparticular sensor pattern, i.e. a mass of air at a different temperatureentering the environment from a single location, or a loss of climatecontrolled air to a single location. These sensor patterns must bedistinguished from other events, such as the action of appliances,movement of individuals in the vicinity of the sensor, a shower andother such events. It is noted that in this instance, a neural networkbased adaptive controller may be more efficient than a standard fuzzylogic system, because the installation and design of such a system iscustom, and therefore it would be difficult to program fuzzy setassociations a priori. In this case, a learning system, such as a neuralnetwork, may be more efficient in operation and produce a better resultthan other adaptive methods. The training procedure may be fullyautomated, (with manual feedback provided where necessary to adjust thecontrol parameters) so long as sufficient sensors are provided forcontrolling the system, and also that an initial presumption of thecontrol strategy is workable during the training period. In the case ofan HVAC system, the initial strategy incorporated is the prior art“bang-bang” controller, which operates as a simple thermostat, ormulti-zone thermostat. As a better starting point, a fuzzy logictemperature controller may be modeled and employed. Other knownstrategies which are not often used in environmental control include theproportional-integral-differential controller (PID).

It is noted that the HVAC system may also be of a type which isinoperable with standard type controllers; for example, the system maybe such as to produce temperature oscillations, or significanttemperature or pressure gradients. In this case, the default controlsystem must be provided to compensate the system, allowing more subtlecorrections and adjustments to be made based on preferences. Thus, anexpert system is provided, which is updated based on user input, andwhich receives context information, including sensor data and otherinputs. Explicit user preferences and programming are also input,preferably with an interface in accordance with the present invention orincorporating aspects thereof In this example, which may be describedwith reference to FIG. 23, sufficient sensors in a sensor array 2301 areprovided, being light, temperature, humidity, pressure, air flow andpossibly a sensor for determining an event proximate to the sensor, suchas door opening. While a single sensor array 2301 provides input to thepresent control, a plurality of sensor arrays are preferably employed incomplex installations, such as that described here. The sensors, withthe possible exceptions of the flow sensor and event sensor, are housedin a single sensor head. Further, the temperature and pressure sensorsmay be combined in a single integrated circuit by known means. The lightand temperature sensors are known to those skilled in the art, and neednot be described herein. The pressure sensor may be a Sensym strain gagepressure transducer, a Motorola pressure transducer device, or the like,which are known in the art. Alternatively, other types of sensors may beused, for example a micromachined silicon force balance pressuretransducer, similar in electrical design to the Analog Devicesmonolithic accelerometers, ADXL-50 or ADXL-05.

The humidity sensor is preferably an electronic type, producing anelectrical signal output. It need not be internally compensated for theother measured environmental factors, as the constellation of sensorsmay compensate each other. The air flow sensor may be based on pressuredifferentials, using the electronic pressure sensor described above, ormay be a mechanical vane type, which is based on flows. In mostapplications, a single flow axis will be sufficient, however, in somecircumstances, a two or greater axis sensor will be required. Further,in the case of large volume areas, complex turbulent flow patterns maybe relevant, for which known sensors exist. Laser based air flow sensorsmay be employed, if desired. LIDAR sensors may be used to determine flowrate, direction, and turbulence.

The event sensor may be of any type, and depends particularly on theevent being measured. In the present case, where a door opening is to bedetected, it is preferred that the environmental control be interfacedwith a perimeter intrusion alarm system, which, for example, provides amagnet embedded in the door and a magnetic reed switch in the doorframe. Individual sensors are normally wired to the alarm control panel,thus providing central access to many or all of the desired eventdetection sensors while minimizing the added cost. The event detectormay also be an ultrasonic, infrared, microwave-Doppler, mechanical, orother type of sensor. Wireless sensors may also be used, communicatingvia infrared beams, acoustic, radio frequency, e.g., 46-49 MHz, 900 MHz,or other bands, using analog, digital or multilevel quantized digitalAM, FM, PSK, QAM, or other modulation scheme, or a combination thereof.Spread spectrum devices may be employed, as well as time, code orfrequency multiplexing or a combination thereof. Various failsafemechanisms are preferably included, including those identifyingtransmitter or receiver failure, communication interference or messagecollision, and other conditions. A reverse communication channel mayalso be included, either symmetric in band, or asymmetric in band or outof band, for communication with the sensor or apparatus associated withthe sensor, and as part of the failsafe system. A forward errorcorrection protocol is preferably effected, which may detect errors andinclude error correcting codes for digital transmissions. Digital datamay be encrypted, and the transmission modulation scheme may alsoinclude an encrypted sequence of frequency, phase, convolution, noise,or other modulation parameter.

While wireless data transmission as described above may be used, thepreferred method of receiving sensor information is through a serialdigital or analog (i.e., 4-20 mA transmitter) data transmission whichmay be multiplexed and/or part of a local area network scheme, withminimal local processing of the sensor data by the microprocessor 2302with the serial link 2302 a in the sensor head. Such serial digitalprotocols and physical transport layers include Echelon LON-works, BSRX-10, CEBUS, RS-232, RS-423, Apple ADB, Appletalk, Ethernet (10 base T,10 Base 2, 10 base 5, 100 Base T, 100 base VG), ATM, USB, IEEE-1394,Homerun (Intel/Tut), etc. This system allows the central control 2303 toincorporate the desired processing, e.g., by the pattern recognitionsystem 2304, etc., while minimizing the installation expense. A simplemicroprocessor device 2302 in the sensor head interfaces the sensingelements, and may provide analog-to-digital conversion, or otherconversion which may be necessary, of the sensor signal. In the case ofa serial digital data transmission, the local microprocessor formats thesensor data, including a code indicating the sensor serial number andtype, the sensor status (i.e., operative, defective, in need ofmaintenance or calibration, etc.), the sensor data, and an errorcorrecting code. In the case that the data is transmitted on a localarea network, the microprocessor also arbitrates for bus usage and themessaging protocol.

The control, it must be understood, has a number of available operativesystems at its disposal, comprising the plant 2306. In this case, thesystem is a forced air heating and cooling system. This system has aheating unit, a humidifier, blowers, a cooling unit (which alsodehumidifies), ducts, dampers, and possible control over variouselements, such as automated door openers.

As described above, the system is installed with a complete array ofsensors, some of which may be shared with, or a part of, other controlsystems in the environment, and begins operation with a basic acceptableinitial control protocol. The system then receives data from thesensors, and correlates data from the various sensors, including theevent sensors, with the operation of the systems being controlled. Insuch a case, a “door open” event may be correlated with a change inother measured variables. The system then correlates the control statuswith the effect on the interrelation of the measured variables. Thus,the system would detect that if the blower is operating while the dooris open, then there is a high correlation that air will flow out of thedoor, unless a blower operates to recirculate air from a return near thedoor. Thus, the system will learn to operate the proximate return devicewhile the door is open and the blower is on. Once this correlation isdefined, the system may further interrelate the variables, such as awind speed and direction outside the door, effects of other events suchas other open doors, the absolute and relative speeds of the blowers andthe return device, the effect of various damper devices, etc. It isfurther noted that, under some circumstances, an exchange of air throughan open door is desired, and in such instance, the system may operate tofacilitate the flow through such an open door. Finally, the system mustbe able to “learn” that conditions may exist which produce similarsensor patterns which should be handled differently. An example is abroken, defective or inoperative sensor. In such a case, the system mustbe able to distinguish the type of condition, and not execute anaggressive control algorithm in an attempt to compensate for anerroneous reading or otherwise normal event. This requires theintelligent control of the present invention. In order to distinguishvarious events, sensors which provide overlapping or redundantinformation, as well as providing a full contextual overview, should beprovided as a part of the system.

It is further noted that energy efficiency is a critical issue inclimate control systems, and an absolute and continuous control over theinternal environment may be very inefficient. Thus, the starting oflarge electrical motors may cause a large power draw, and simultaneousstarting of such equipment may increase the peak power draw of afacility, causing a possible increase in the utility rates. Further,some facilities may operate on emergency or private power generation(co-generation) which may have different characteristics and efficiencycriteria. These factors may all be considered in the intelligentcontrol. It is also noted that a higher efficiency may also be achieved,in certain circumstances, by employing auxiliary elements of the climatecontrol system which have a lower capacity and lower operating coststhan the main elements. Thus, for example, if one side of a building isheated by the sun, it may be more efficient to employ an auxiliarydevice which suitably affects, i.e. compensates, only a part of thebuilding. If such equipment is installed, the aggregate efficiency ofthe system may be improved, even if the individual efficiency of anelement is lower. Likewise, it may be preferable to run a 2-½ ton airconditioning unit continuously, rather than a 5 ton air conditioningunit intermittently. The present intelligent control allows a finedegree of control, making use of all available control elements, in anadaptive and intelligent manner.

Returning to the situation of a door opening event, the system wouldtake appropriate action, including: interruption of normal climatecontrol until after the disturbance has subsided and normal conditionsare achieved; based on the actual climatic conditions or predictedclimatic conditions begin a climate compensation control, designed tomaximize efficiency and also maintain climatic conditions during thedisturbance, as well as return to normal after the disturbance;optionally, during the door opening disturbance, the system wouldcontrol a pressure or flow of air to counterbalance a flow through thedoor, by using a fan, blower or other device, or halting such a device,if necessary. It is also noted that the climatic control system couldalso be outfitted with actuators for opening and closing doors andwindows, or an interface with such other system, so that it could takedirect action to correct the disturbance, e.g., by closing the door. Theclimate between the internal and external ambients may differ intemperature, humidity, pollutants, or the like, and appropriate sensorsmay be employed.

It is thus realized that the concepts of using all available resourcesto control an event, as well as using a predictive algorithm in order todetermine a best course of action and a desired correction are a part ofthe present invention.

EXAMPLE 19

Remote Control Hardware

A remote control of the present invention may be constructed from, forexample, a Micromint (Vernon, Conn.) RTC-LCD, RTC-V25 or RTC-HC11 orRTC180 or RTC31/52, and RTC-SIR, in conjunction with an infraredtransmitter and receiver, input keys and a compatible trackball, whichmay provide raw encoder signals, or may employ a serial encoder and havea serial interface to the processor module. A power supply, such as abattery, is used. The use, interfacing and programming of such devicesis known to those skilled in the art, and such information is generallyavailable from the manufacturer of the boards and the individual circuitelements of the boards. The function of such a remote control is toreceive inputs from the trackball and keys and to transmit an infraredsignal to the controller.

The processor and display, if present, may provide added functionalityby providing a local screen, which would be useful for programmingfeedback and remote control status, as well as compressing the datastream from the trackball into a more efficient form. In this case,certain of the extracted information may be relevant to thedetermination of the user level, so that information related to the userlevel would be analyzed and transmitted separately to the controller bythe infrared transmitter. If the local LCD screen is used in theprogramming process, then the main controller would transmit relevantinformation to the remote display, by a reverse-channel infrared link.These components are known in the art, and many other types may also beused in known manner.

In known manner, available personal digital assistants (“PDAs”),available from 3 Com (Palm Pilot III), Microsoft Windows CE-baseddevices, Apple (“Newton” model 100, 110, 120), Tandy, Poquet, Sharp,Casio, AT&T (Eo 440), Hewlett-Packard, etc. may also be employed as ahuman interface device.

EXAMPLE 20

Medical Device Interface

The interface and intelligent control of the present invention areapplicable to control applications in medicine or surgery. This systemmay also be described with reference to the generic system drawings ofFIGS. 23 and 24. In this case, an operator identifies himself and entersinformation regarding the patient, through the interface 2305. Theinterface 2305 automatically loads the profile 2406 of both the operatorand the patient, if the device is used for more than one at a time, andis connected to a database containing such information, such as ahospital central records bureau. The interface may be connected tovarious sensors, of the input device 2401, such as ambient conditions(temperature, humidity, etc.), as well as data from the patient, such aselectrocardiogram (EKG or ECG), electromyograph (EMG),electroencephalogram (EEG), Evoked Potentials, respirator, anesthesia,temperature, catheter status, arterial blood gas monitor, transcutaneousblood gas monitor, urinary output, intravenous (IV), intraperitoneal(IP), Intramuscular (IM), subcutaneous (SC), intragastric or other typesof solutions, pharmaceutical and chemotherapy administration data,mental status, movement, pacemaker, etc. as well as sensors and datasources separate from the patient such as lab results, radiology andmedical scanner data, radiotherapy data and renal status, etc. Based onthe available information, the interface 2405, using the simple inputdevice and the display screen described above, presents the mostimportant information to the operator, along with a most probable courseof action. The user then may either review more parameters, investigatefurther treatment options, input new data, or accept the presentedoption(s). The system described has a large memory in the signalanalysis module 2409 for recording available patient data from thesignal receiver 2408, and thus assists in medical record keeping anddata analysis, as well as diagnosis. While various systems are availablefor assisting in both controlling medical devices and for applyingartificial intelligence to assist in diagnosis, the present systemallows for individualization based on both the service provider and thepatient. Further, the present invention provides the improved interfacefor interaction with the system.

It is further noted that, analogously to the library function discussedabove, medical events may be characterized in the characterization unit2407 and recorded by the plant 2404, so that a recording of the dataneed not be reviewed in its entirety in order to locate a particularsignificant event, and the nature of this event need not be determinedin advance. It is also noted that the compression feature of therecorder of the present invention could be advantageously employed withthe large volume of medical data that is often generated. Medical dataimage data may be compressed as known in the art, by standard imagecompression techniques, and/or image compression techniques optimizedfor radiology, nuclear medicine and ultrasonography data. Other types ofdata may be compressed using lossless algorithms, or by various vectorquantization, linear excited models, or fractal compression methods. Itis finally noted that, because of its ability to store and correlatevarious types of medical data in the characterization unit 2407, thesystem could be used by the operator to create notes and dischargesummaries for patients, using the database stored in the local database2413, as well as the user history and preferences 2406. Thus, inaddition to saving time and effort during the use of the device, itwould also perform an additional function, that of synthesizing thedata, based on medical signiflcance.

In addition to providing the aforementioned intelligence and ease ofuse, the present example also comprises a control 2402, and mayinterface with any of the sensors and devices, performing standardcontrol and alarm functions. However, because the present control 2402is intelligent and has pattern recognition capability, in addition tofull data integration from all available data sources, it may executeadvanced control functions. For example, if the present control 2402 isinterfaced to a controlled infusion pump for, e.g., morphine solution,in e.g., a terminally ill patient, then certain parameters must bemaintained, while others may be flexible. For example, a maximum flowrate is established as a matter of practice as a safety measure; toohigh a flow rate could result in patient death. However, a patient maynot need a continuous infusion of a constant dose of narcotic. Further,as the patient's status changes, the level of infusion may beadvantageously altered. In particular, if the renal status of thepatient were to change, the excretion of the drug may be impaired.Therefore, by providing the controller with a urinary output monitor, itcould immediately suppress the morphine infusion as soon as the renaloutput is recognized as being decreased, and further indicate an alarmcondition. Further, it may be advantageous to provide a diurnalvariation in the infusion rate, to provide a “sleep” period and a periodof heightened consciousness with correspondingly lower levels ofnarcosis. Where various tests, procedures or interviews are scheduled,an appropriate level of narcosis and/or analgesia may also beanticipatorily provided at an appropriate time.

As another example of the use of the present device as a medicalcontroller, the control 2402 could be interfaced with a cardiac cathetermonitor, as a part of the signal receiver 2408. In such a case,normally, alarms are set based on outer ranges of each sensormeasurement, and possibly a simple formula relating two sensormeasurements, to provide a useful clinical index. However, byincorporating the advanced interface and pattern recognition function ofthe present invention, as well as its ability to interface with avariety of unrelated sensors, the present device, including the presentcontrol, may be more easily programmed to execute control and alarmfunctions, may provide a centralized source of patient information,including storage and retrieval, if diverse sources of such informationare linked, and may execute advanced, adaptive control functions. Thepresent control 2402 is equipped to recognize trends in the sensor datafrom the signal receiver 2408, which would allow earlier recognition andcorrection of various abnormal conditions, as well as recognizingimprovements in conditions, which could allow a reduction in thetreatment necessary. Further, by allowing a fine degree of control,parameters may be maintained within optimal limits for a greaterpercentage of the time. In addition, by monitoring various sensors,various false alarms may be avoided or reduced. In particular, falsealarms may occur in prior art devices even when sensors do not indicatea dangerous condition, merely as a safety precaution when a particularparameter is out of a specified range. In such a case, if a cause ofsuch abnormal condition may be identified, such as patient movement orthe normal activities of the patient's caretakers, then such conditionmay be safely ignored, without indicating an alarm. Further, even if asensor parameter does in and of itself indicate a dangerous condition,if a cause, other than a health risk, may be identified, then the alarmmay be ignored, or at least signaled with a different level of priority.By providing an intelligent and active filter for false alarm events,the system may be designed to have a higher level of sensitivity andspecificity to real health risks, and further to provide a finer levelof control based on the sensor readings, with fewer false positivereadings.

EXAMPLE 21

Securities Trading Terminal Interface

The present invention is also of use in automated securities, debt,variable yield and currency trading systems, where many complexfunctions are available, yet often a particular user under particularcircumstances will use a small subset of the functionality available ata given time. Such a situation would benefit from the present interface,which provides adaptive user levels, prioritized screen informationpresentation, and pattern recognition and intelligent control. Asecurities trading system is disclosed in U.S. Pat. No. 5,034,916, for amouse driven Fast Contact Conversational Video System, incorporatedherein by reference. The present system relates primarily to the userterminal, wherein the user must rapidly respond to external events, inorder to be successful. In such a case, the advantages of theapplication of an interface according to the present invention areobvious, and need not be detailed herein. However, the patternrecognition functions of the present invention may be applied tocorrespond to the desired actions of the trader, unlike in priorintelligent trading systems, where the terminal is not individually andadaptively responsive to the particular user. Thus, the system exploitsthe particular strengths of the user, facilitating his actions,including: providing the desired background information and tradinghistories, in the sequence most preferred by the user; following thevarious securities to determine when a user would execute a particulartransaction, and notifying the user that such a condition exists;monitoring the success of the user's strategy, and providing suggestionsfor optimization to achieve greater gains, lower risk, or otherparameters which may be defined by the user. Such a system, rather thanattempting to provide a “level playing field” to all users of liketerminals, allows a user to use his own strategy, providing intelligentassistance. By enhancing the interface, a user becomes more productivewith fewer errors and faster training.

EXAMPLE 22

Fractal Theory Pattern Recognition

Affine transforms are mathematical manipulations of data in twodimensions, wherein the manipulation comprises a rotation, scaling and adisplacement for each of the two coordinates. Schroeder, M., Fractals,Chaos, Power Laws, W.H. Freeman & Co., New York (1991). Of course,Affine transforms of higher dimensionality may also be employed. Indescribing an image using Affine transforms, the degree of matchingbetween an image and the mathematical description of that image may berelated by a number of iterations, and the fewer the iterations, theless data used to describe the image. Of particular importance in thefield of graphics is the speed of “convergence”, i.e., that a relativelyfew iterations are necessary in order to describe an image withsufficient precision to be visually useful. Therefore, the Affinetransform mathematical specifications may be far more compact than theraw image data, and these specifications compare favorably to othertypes of image compression, such discrete cosine transformation (DCT)compression schemes, including JPEG, depending on a number of factors.

Because the Affine transform may be used to produce a compact visualdescription of an image, among other reasons, the present inventionapplies this transform to a pattern matching system for analyzing imagecontents.

Pattern recognition, in this case, may proceed on an image basis, tomatch similar images, or on an object basis, in which portions of imagesare matched. It is preferred that the pattern matching system be robust,i.e., tolerant of various alterations of an image, artifacts,interference and configurational changes, while specific enough to allowobject differentiation.

In the case of video images, therefore, it is preferred that varioustwo-dimensional projections of three-dimensional objects, in various“poses”, be classified the same. This therefore requires that, inanalyzing a two-dimensional image, the object be extracted from abackground image and separated from other objects. Further, degrees offreedom may be determined, such as through analysis of a sequence offrames to reveal relative motion or change of portions of the objectwith respect to other portions. Finally, the object in the image must becompared to three dimensional models, through various projections.

In the case of two dimensional image analysis, the image should beanalyzed according to a robust starting criteria, so that the similarityof images may be determined by comparison of normalized Affinetransformation coefficients.

Fractal analysis, the study of self-similarity, and a superset of Affinetransformation, allows a compact representation of an image or an objectin an image, and due to its encompassing of various spatialrelationships of object parts, allows normalized transforms to becompared. In other words, assuming that the object is extracted from abackground scene, and various degrees of freedom are identified, anAffine transformation may be applied, which will yield a similar resultfor an image of the same object in a different “pose”, i.e., withdifferent exercise of its degrees of freedom. While in general, Affinetransformations are described with respect to two-dimensional images,these may also be applied to three dimensional images. Thus, if atriangular polygon is rotated, scaled and displaced in a two dimensionalimage, a tetrahedron is rotated, scaled and displaced in a threedimensional system. Further, analogies may also be drawn to the timedimension (although geometric forms which are rotated, scaled anddisplaced over time are not given trivial names). Because, in acontractive Affine transformation (one in which the scaling factor ofsuccessive iterations is less than 1), continued iterations are lesssignificant, objects described with varying level of detail may becompared. Even images which are not normalized may still be compared,because at every level of the transform, slight changes in rotation,scale and displacement are accounted for.

According to the present invention, nonlinear self-similarity may alsobe used. Further, in objects having more than two dimensions, linearscaling other than rotation, scaling and displacement may be described.

It is noted that many types of optical computers, especially thoseincluding holographic elements, employ transformations similar to Affinetransformations. Therefore, techniques of the present invention may beimplemented using optical computers or hybrid optical-electroniccomputers.

Thus, according to the present invention, the fractal method employingAffine transforms may be used to recognize images. This method proceedsas follows. A plurality of templates are stored in a memory device,which represent the images to be recognized. These templates may bepreprocessed, or processed in parallel with the remainder of theprocedure, in a corresponding manner. Image data, which may be highcontrast line image, greyscale, or having a full color map, thegreyscale being a unidimensional color map, is stored in the dataprocessor, provided for performing the recognition function.

The image is preprocessed to extract various objects from thebackground, and to separate objects. This preprocessing may be performedin standard manner. The method of U.S. Pat. No. 5,136,659, incorporatedherein by reference, may also be used. As a part of this preprocessing,a temporal analysis of the object through a series of image frames, isperformed to provide four dimensional data about the object, i.e., thetwo dimensions from the image, a third image imputed from differingperspective views of the object, and time. Certain objects may beimmediately recognized or classified, without further processing.Further, certain objects, without full classification or identification,may be “ignored” or subjected to a lesser level of final processing.During the classification processing, various objects may be selectedfor different types of processing, for example, people, automobiles,buildings, plants, etc.

After classification, and temporal analysis, an object for furtherprocessing is analyzed for degrees of freedom, i.e., joints of a person,moving parts of an object, etc. These degrees of freedom may then becorrected, e.g., the object itself altered, to change the image into astandard format, or the degree of freedom information processed with theobject to allow mathematical normalization without actual change of theimage.

The information describing the object image is stored. A plurality ofaddressable domains are generated from the stored image data, each ofthe domains representing a portion of the image information. As notedabove, the entire image need not be represented, and therefore variousobjects separately analyzed. Further, only those parts of the image orobject necessary for the recognition, need be analyzed. While it may beunknown which image components are unnecessary, sometimes this may bedetermined.

From the stored image data, a plurality of addressable mapped ranges arecreated, corresponding to different subsets of the stored image data.Creating these addressable mapped ranges, which should be uniquelyaddressable, also entails the step of executing, for each of the mappedranges, a corresponding procedure upon the one of the subsets of thestored image data which corresponds to the mapped ranges. Identifiersare then assigned to corresponding ones of the mapped ranges, each ofthe identifiers specifying, for the corresponding mapped range, aprocedure and a address of the corresponding subset of the stored imagedata.

To ensure comparability, the processing treatment of the template andthe image data are analogous. Of course, template data may be stored inpreprocessed form, so that the image data need only be processedaccording to the same rules. The domains are optionally each subjectedto a transform, which may be a predetermined rotation, an inversion, apredetermined scaling, and a displacement. Because of the nature ofthese linear superposable transforms, the earliest iterations willinclude data about gross morphology, later iterations will include dataabout configuration, and latest iterations will include data abouttexture.

In addition, nonlinear alterations, and frequency, Gabor or wavelettransform preprocessing may be applied. A warping or other kind oftransform may also be applied. These types of transforms are generallynot included in Affine transform analysis, yet judiciously applied, mayproduce more rapid convergence, greater data storage efficiency,computational advantages or pattern matching advantages.

This transform is used to optimize the procedure, and also to conformthe presentation of the image data with the template, or vice versa.Each of the domains need not be transformed the same way, and in fact itis the transform coefficients which are stored to describe thetransformed object, so that differences in coefficients relate todifferences in objects.

For each of the domains or transformed domains, as may be the case, theone of the mapped ranges which most closely corresponds according topredetermined criteria, is selected. The image is then represented as aset of the identifiers of the selected mapped ranges.

Finally, from the stored templates, a template is selected which mostclosely corresponds to the set of identifiers representing the imageinformation. This matching process is optimized for the data type, whichis a string of iterative transform coefficients, of a contractivetransform.

It is preferred that, for each domain, a most closely corresponding oneof the mapped ranges be selected. By performing analogous operations ona template and an unrecognized object in an image, a correspondencebetween the two may be determined. Thus, libraries of template imageportions may be provided, with associated transform information, whichmay increase the computational efficiency of the system.

In selecting the most closely corresponding one of the mapped ranges,for each domain, the mapped range is selected which is the most similar,by a method which is appropriate, and may be, for example, selectingminimum Hausdorff distance from the domain, selecting the highestcross-correlation with the domain, the minimum mean square error withthe domain and selecting the highest fuzzy correlation with the domain,based on rules which may be predetermined. Neural network energyminimization may also yield the best fit, and other techniques may alsobe appropriate.

In particular, the step of selecting the most closely corresponding oneof mapped ranges according to the minimum modified Hausdorff distanceincludes the step of selecting, for each domain, the mapped range withthe minimum modified Hausdorff distance calculated asD[db,mrb]+D[1−db,1−mrb], where D is a distance calculated between a pairof sets of data each representative of an image, db is a domain, mrb isa mapped range, 1−db is the inverse of a domain, and 1−mrb is an inverseof a mapped range.

It is important that the selection criteria be tolerant to variations ofthe type seen in image data, e.g., video, so that like objects havesimilar transforms. Thus, the selection criteria is not particularlydirected to optimal data compression, although the two criteria maycoincide for some types of data.

In the case where the digital image data consists of a plurality ofpixels, each having one of a plurality of associated color map values,the method includes a matching of the color map, which as stated above,encompasses a simple grey scale, natural color representation, and othercolor types. In such a case, the method is modified to optionallytransform the color map values of the pixels of each domain by afunction including at least one scaling function, for each axis of thecolor map, each of which may be the same or different, and selected tomaximize the correspondence between the domains and ranges to which theyare to be matched. For each of the domains, the one of the mapped rangeshaving color map pixel values is selected which most closely correspondsto the color map pixel values of the domain according to a predeterminedcriteria, wherein the step of representing the image color mapinformation includes the substep of representing the image color mapinformation as a set of values each including an identifier of theselected mapped range and the scaling functions. The correspondencemethod may be of any sort and, because of the added degree ofcomplexity, may be a different method than that chosen for non-colorimages. The method of optimizing the correspondence may be minimizingthe Hausdorff distance or other “relatedness” measurement between eachdomain and the selected range. The recognition method concludes byselecting a most closely corresponding stored template, based on theidentifier of the color map mapped range and the scaling functions,which is the recognized image.

Color information may have less relevance to pattern recognition than,for example, edge information, and therefore may be subjected to alesser degree of analysis. The color information may also be analyzedseparately, using a different technique.

EXAMPLE 24

Image Analysis

Alternatively to the object extraction, the image as a whole may beanalyzed. In the case of moving images, the aforementioned method isfurther modified to accommodate time varying images. These imagesusually vary by small amounts between frames, and this allows astatistical improvement of the recognition function by compensating fora movement vector, as well as any other transformation of the image.This also allows a minimization of the processing necessary becauseredundant information between successive frames is not subject to thefull degree of processing. Of course, if the image is substantiallychanged, then the statistical processing ceases, and a new recognitionfunction may be begun, “flushing” the system of the old values. Thebasic method is thus modified by storing delayed image data information,i.e., a subsequent frame of a moving image. This represents an image ofa moving object differing in time from the image data in the dataprocessor.

A plurality of addressable further domains are generated from the storeddelayed image data, each of the further domains representing a portionof the delayed image information, and corresponding to a domain. Thus,an analogous transform is conducted so that the further domains each arecorresponding to a domain. A plurality of addressable mapped rangescorresponding to different subsets of the stored delayed image data arecreated from the stored delayed image data. The further domain and thedomain are optionally matched by subjecting a further domain to acorresponding transform selected from the group consisting of arotation, an inversion, a scaling, and a displacement, which correspondsto a transform applied to a corresponding domain, and a noncorrespondingtransform selected from the group consisting of a rotation, aninversion, a scaling, a translation which does not correspond to atransform applied to a corresponding domain. For each of the furtherdomains or transformed further domains, the one of the mapped ranges isselected which most closely corresponds according to predeterminedcriteria. As stated above, these domains may also be subjected tocorresponding and noncorresponding frequency domain processingtransforms, Gabor transforms, and wavelet transforms.

A motion vector is then computed between one of the domain and thefurther domain, or the set of identifiers representing the imageinformation and the set of identifiers representing the delayed imageinformation, and the motion vector is stored. The further domain iscompensated with the motion vector and a difference between thecompensated further domain and the domain is computed. For each of thedelayed domains, the one of the mapped ranges is selected which mostclosely corresponds according to predetermined criteria. The differencebetween the compensated further domain and the domain is represented asa set of difference identifiers of the selected mapping ranges and anassociated motion vector.

This method is described with respect to FIGS. 27, 28 and 29. FIG. 27 isa basic flow diagram of the recognition system of the present invention.FIG. 28 provides a more detailed description, including substeps, whichare included in the major steps shown in FIG. 27. Basically, the image,or a part thereof, is decomposed into a compressed coded version of thescene, by a modified fractal-based compression method. In particular,this differs from the prior compression algorithms in that only a part,preferably that part containing objects of interest, need be fullyprocessed. Thus, if a background is known (identified) or uninteresting,it may be ignored. Further, the emphasis is on matching the availabletemplates to produce an image recognition, not achieving a high degreeof compression. Therefore, the image, or domains thereof, may betransformed as required in order to facilitate the matching of thetemplates. As with respect to single images, the templates arerepresented in analogous form, having been processed similarly, so thata comparison of the relatedness of an object in an image and thetemplates may be performed. In particular, if an oblique view of anobject is presented, then either the object may be transformed toachieve a predicted front view, or the template transformed or speciallyselected to correspond to the oblique view. Further, once a recognitionprocess has taken place with a high degree of certainty, the system needonly ensure that the scene has not changed, and need not continuallyfully process the data. This has implications where multiple recognitionprocesses are occurring simultaneously, either in a single scene or indifferent images, wherein the throughput of the recognition apparatusneed not meet that required for de novo real time recognition of allaspects of all the objects or images.

In order to limit processing of portions of images, exclusionarycriteria may be applied which allow truncation of processing when it isdetermined that an option is precluded or there exists a significantlyhigher probability alternative. The processing system may use primarilyexclusionary criteria to select the best predictions, or afterpreselection, employ a highest probability selection system on theremaining choices.

FIG. 30 shows a flow diagram of a cartoon-like representation of animage recognition method of the present invention. It shows initially,an input image 3001, having a degree of complexity. A windowing function3002 isolates the object from the background. A first orderapproximation of the image is generated 3003, here called a mappingregion. The first order approximation is then subtracted from theinitial image to produce a difference 3004. The first order error isthen subjected, iteratively, to successive transform and subtractoperations 3005 and 3006, until the error is acceptably small, at whichpoint the input image is characterized by a series of codes,representing the first order approximation and the successivetransforms, which are stored 3008. These codes are then compared withstored templates 3009. The comparisons are then analyzed to determinewhich template produces the highest correlation 3010, and the matchprobability is maximized 3011. The recognized image is then indicated asan output 3012.

This system is shown in FIG. 26, wherein a sensor 2602 provides data,which may be image data, to a control 2601. The control 2601 serves tocontrol the plant 2603, which has an actuator. The plant 2603 may be aVCR or the like. The control 2601 has associated with it an intermediatesensor data storage unit 2611, which may be, for example a frame bufferor the like. The control 2601 also has associated with it a transformengine 2612, which may perform a reversible or irreversible transform onthe data or stored data.

The system also has a template input 2610, which may receive data fromthe sensor 2602, if accompanied by identifying information. Thus, thepattern storage memory 2609 stores a pattern, such as an image pattern,along with an identifier.

The control 2601 also has an input device 2604, an on-screen displayinterface 2605, and a program memory 2606, for inputting instructionsfrom a user, providing feedback to the user, and recording the result ofthe user interaction, respectively. Finally, a characterization network2607 characterizes the sensor 2602 data, which may be provided directlyfrom the sensor 2602 or preprocessing circuitry, or through the control2601. A correlator 2608 correlates the output of the characterizationnetwork with the stored patterns, representing the templates from thetemplate input 2610. The system therefore operates to recognize sensorpatterns, based on the correlator 2608 output to the control 2601.

When analyzing objects in a sequence of images, a determination is madeof the complexity of the difference based on a density ofrepresentation. In other words, the error between the movement andtransform compensated delayed image and the image is quantified, todetermine if the compensation is valid, or whether the scene issignificantly changed. When the difference has a complexity below apredetermined or adaptive threshold, a template is selected, from thestored templates, which most closely corresponds or correlates with boththe set of identifiers of the image data and the set of identifiers ofthe delayed image data, thus improving recognition accuracy, by allowinga statistical correlation or other technique. The threshold may be setbased on an error analysis of the system to determine statisticalsignificance or using other criteria. The threshold may also beadaptively determined based on the history of use of the machine andfeedback. For example, if the two images both have a high correlationwith one template, while a first of the images has a slightly highercorrelation with another template, while the second image has a muchlower correlation with that other template, then the system would scorethe first template as a better match to the first image, based on thisdifferentiation. Thus, templates may be particularly selected to bestdifferentiate similar images of objects.

EXAMPLE 25

Pattern Recognition System

The present system allows for the use of a pattern recognition subsystemfor a controller which acts in accordance with a detected pattern. Inimage, audio and multimedia applications, different types of imageprocessing may take place. First, various processing algorithms may takeplace in parallel, with an optimum result selected from the results ofthe various algorithms. Further, various processing schemes may beapplied in sequence, with differing sequences applied to different datastreams. These processing schemes may be commutative, i.e. yieldapproximately the same result regardless of the processing order, or maybe highly order dependent, in which case a processed data stream mustinclude information relating to the sequence of processing forinterpretation.

Various exemplars may reside in a fragment library, for comparison withunidentified data. In the case of processing path dependent systems, anexemplar may be found in multiple forms based on the processingprocedure, or in a small subset of corresponding libraries. In general,both lossless compression methods and lossy compression methods employedusing high fidelity parameters to minimize loss may be processed toproduce a relatively or almost unique result for each unknown data set,while lossy compression or processing methods will be particularlyprocedure sensitive, especially if differing strategies are employed.These differing strategies may be used to emphasize different featuresof the unknown data set in order to facilitate comparison. Thistechnique is especially useful when the processing procedures are run inparallel, so that the latency penalty for redundant processing isminimized. Techniques available for this processing includevectorization, fractal processing, iterated function systems, spatialfrequency processing (DCT-JPEG, MPEG, etc.), wavelet processing, Gabortransforms, neural nets (static or sequence of images), and other knowntechniques.

In a preferred embodiment, a spatial frequency or wavelet processingstep is performed first, on static image data or a sequence of images,with a fractal domain processing step performed thereafter. This allowshigh frequency noise to be initially filtered; with subsequentfractal-based correlated noise detection and subtraction, thereforeallowing cleanup without loss of high frequency detail. Preferably,before the fractal-based processing, which may be performed by a digitalcomputer or optical processing apparatus, standard edge detection/objectseparation, e.g., high frequency filtering, contour mapping, artificialintelligence, etc. may be performed. A fractal transform is thenperformed on the image of a portion thereof, starting in a standardizedmanner, e.g. at a point of lowest complexity, or the epicenter of thelargest feature for beginning a contractive transform. The processedimage may then be matched with one or more databases to identify all ora portion of the image. Optionally, after a match has been found and/orconfirmed by an operator, using the human interface system, the methodis then optimized to minimize the errors and increase the efficiency oflater matches. This may be performed by modifying the database record,or related records, as well as modifying the preprocessing algorithm. Ina preferred embodiment, the image is processed piecemeal, on anobject-by-object basis. Therefore, after an object has been processed,it is extracted from the image so that the remaining information may beprocessed. Of course, multiple objects may be processed in parallel. Theexemplar database is preferably adaptive, so that new objects may beadded as they are identified.

The present technology may also be used with a model-based exemplardatabase, wherein an image object is matched, based on a two dimensionalprojection, or analysis of a sequence of images, with a multidimensionalmodel of an object. For example, the model may include volume, as wellas multiple degrees of freedom of movement. Further, objects may alsoinclude “morphing” characteristics, which identify expected changes inan appearance of an object. Other types of characteristics may beincluded in conjunction with the exemplar in the database.

In a preferred embodiment, a model contained in a database includes athree or more dimensional representation of an object. These modelsinclude information processed by a fractal-based method to encoderepetitive, transformed patterns in a plane, space, time, etc., as wellas to include additional degrees of freedom, to compensate for changesin morphology of the object, to allow continuous object identificationand tracking. Thus, once an object is identified, an expected change inthat object will not necessitate a reidentification of the object.According to one embodiment, a fractal-like processing is executed byoptical elements of an optical or optical hybrid computer. Further, inorder to temporarily store an optical image, optically active biologicalmolecules, such as bacteriorhodopsins, etc. may be used. Liquid crystalsor other electrophotorefractive active materials may also used. Theseimagers may be simple two dimensional images, holograms, or otheroptical storage methods. A preferred holographic storage method is avolume phase hologram, which will transform an impressed image, based onhologram to image correlation. Thus, these models would be somewhatlinear transform independent, and would likely show some (planar)transform relationship. Thus, an optical computer may be advantageousbecause of its high computational speed as compared to digital computersfor image analysis, due to inherent parallelism and high inherent speed.

Because of the present limitations in speed of writing an image tooptical recording media, especially holographic images, the preferredsystem includes a plurality of image storage elements, which areoperated in parallel. It is noted that absolute accuracy of objectidentification is not required for “consumer” applications, andtherefore partial match results may be considered useful. A plurality ofpartial results, when taken together, may also increase identificationreliability. Critical applications generally differ in quantitativeaspects rather than qualitatively, and therefore many aspects of thepresent invention may be applied to mission critical and other highreliability applications.

A preferred object identification method proceeds by first classifyingan object in an image, e.g., “car”, “person”, “house”, etc. Then, basedon the classification and object separation, an optimized preprocessingscheme is implemented, based on the classification. This classificationpreprocessing operates on the raw image data relating only to theobject, separated from the background. Then, after the optimizedpreprocessing, a parallel recognition system would operate to extractunique features and to identify common features to be excluded from thecomparison. This step could also identify variable features upon whichidentification should not be made because the distinctions are uselessfor the purpose. Thus, the object image at this point loses itsrelationship to the entire image, and the data reduction might besubstantial, providing a compact data representation. The preferredalgorithm has a tree structure, wherein the identification need onlydifferentiate a few possibilities, and pass the result to another branchof the tree for further analysis, if necessary. Since the intermediatecalculations may help in later computations, these should preferably beretained, in order to avoid duplicative analysis. Further, the order ofanalysis should be predetermined, even if arbitrary, so that once auseful intermediate calculation is identified, it may be passed in aregular, predictable manner to the next stage processing. Of course, oneshould not ignore that objects in the entire image may be correlatedwith one another, i.e. if one object is present, it would increase ordecrease the likelihood of another object also being present. Further,temporal correlations should also be noted. Thus, the objectidentification need not proceed upon each object independently.

Based on time sequences of two-dimensional images, a three dimensionalimage representation may be constructed. Alternatively, based on variouspresumptions about extractable “objects” in a single or small group oftwo dimensional images, a hypothetical three dimensional object may bemodeled, which may be later modified to reflect the actual image when anactual view of hidden surfaces is shown. Therefore, by one means oranother a three dimensional model is created, having both volume andsurface characteristics. Of course, since inner structure may never beseen, the model normally emphasized the surface structure, and is thus aso-called two-and-a-half dimensional surface model. Other non-integraldimension representations may also be useful, and fractal models mayefficiently represent the information content of an image model.

When the source signal is an MPEG encoded datastream, it is advantageousto provide an exemplar database which does not require completeexpansion of the encoded signal. Thus, the motion vector analysisperformed by the MPEG encoder may form a part of the pattern recognitionsystem. Of course, image sequence description formats other than MPEGmay be better suited to pattern analysis and recognition tasks. Forexample, a system may transmit an interframe, by any suitabledescription method, as well as an object decomposed image in, e.g.,fractal transform codes. The transmitted source material, other thaninterframes, is then transmitted as changes only, e.g. new objects,transforms of existing objects, translations of existing objects, etc.

Color coding may use even more extensive use of fractal compressiontechnology with high compression ratios, because absolute accuracy isnot necessary; rather photorealism and texture are paramount, and neednot be authentic. Therefore, backgrounds with significant detail, whichwould require substantial data in a DCT type system, could be simplycoded and decoded without loss of significant useful information.Important to the use of this method is to discriminate betweenbackground textures and foreground objects, and to encode eachseparately, optimizing the processing based on the type of object beingprocessed.

EXAMPLE 26

Date Context Sensitive Computer Interface

The present example relates to a context sensitive computer interface inwhich a characteristic of the interface is modified based on alinguistic or informational content of a data object upon which theinterface is operating. For example, a number of alternate feature setsmay be made available based on the type of data which is being operatedon by the user. For example, differing feature sets would be optimal foreach scientific discipline, each type of financial or economic field,marketing, retail, distribution, manufacturing, administration, humanresources, etc. Such an interface will make it possible to provide anextended and extensible suite of application modules customized for theuser in general, and further adaptive to the particular use to which theuser may be making of the apparatus. Thus, complex options particularlysuited for the data at hand may be made available without inefficientinterface searching, while inappropriate options are not presented. Itis noted that this interface is responsive to the data, rather than theprogramming. Further, the data is analyzed for its meaning, rather thanits type.

In a word processing environment, a document or section of a document isanalyzed for the presence of particular words or phrases, or for thepresence of concepts, interpretable by linguistic concepts. Thiscontext-sensitive functionality does not require an explicit definitionby the user, but rather will be present even during an incidentaloccurrence of a recognized context. In accordance with other aspects ofthe present invention, each context related function may have varioususer levels, which are selected based on an imputed user level of theuser. Thus, the interface program must actually interpret the text orcontext of the user document in order to select the most likely optionsfor use.

Thus, if a user were to embed a table in a document, the availableoptions would change to table-type options when the “active” portion ofthe document is at the table, i.e. within the viewable area, etc.Further, and more specifically, if the text and context of the tableindicate that this is a financial table, financial options would beinitially provided, and standard financial calculation functionsimmediately made available or performed, in contemplation of theirprospective use. Similarly, if the data appears to be scientific, adifferent set of options would be initially available, and the standardscientific-type calculation functions be made available or performed. Ifthe table relates to chemical or mechanical-type data, chemical ormechanical options might be made available, respectively. Embeddedgraphics, likewise, would be associated with graphics functionsappropriate to the type of graphic. It is noted that, due to theanalysis of the content of the document, software having genericfunctionality may present as special purpose software, based on itsactual use.

Thus, in a like manner, the system could determine the “style” of thedocument and automatically format the data in a predetermined manner toconform with general standards of presentations relating to the desiredstyle. This is similar to style sheets of many programs, but they areself applying, and will, within the same document, be adaptive as thedata changes context. Further, since the “styles” would be appliedautomatically, it would be relatively easy to alter them, requiring onlya small amount of manual effort. This is so because the “keys” by whichthe system determines style could be stored, thus allowingredeterminations to be easily made. This context sensitivity could alsoassist in spelling and grammar checking, where different rules mayapply, depending on the context.

The data object includes information, which might be text, arrays ofnumbers, arrays of formulas, graphics, or other data types. The systemrelates parts of the object to each other by “proximity” which could belinear, in the case of a text document, or otherwise, such as in thecase of a hypertext document or spreadsheet. Those parts or elements ofthe object closest to each other, by whatever criteria, are presumed tobe topically related, regardless of data type. Thus, if a paragraph oftext is proximate to a table for numbers, then the type of numberspresumed to occupy the table would relate to the content of theproximate text. If the text relates to finance, i.e. usesfinancial-related terms, or series of words that often occur infinancial contexts, the table would be presumed to be a financial table.

Once the context of the part of the object is determined, the systemthen acts based upon this context. The major act is the presentation oftailored menus. This means that if the context is financial, the menusavailable for use with the numeric table relate to financial tables orspreadsheets. Further, the proximate text would be subject to financialoriented spellcheck and financial oriented grammar or style check. If agraphics-option is selected proximate to the text and table, the menuoptions would presume a financial graph and present appropriate choices.Of course, the options need not be limited to a few types, and may behybrid and/or adaptive to the style of the user. However, it is notedthat the adaptive menus could be linked to a “corporate style”. Thus,communication styles could be dictated by a set of global rules for anorganization. Of course, these a priori choices could be overridden.

An advantage of this system is that it allows a software system toinclude a wide range of functionality which remains “buried”, orrelatively inaccessible, based on the context of usage. Thus, featurerich software would be considered more usable, and software could beprovided in modular fashion. Since the system might allow a user to havepotential access to many software modules, the system could also belinked to a license manager and per use billing system for rarely usedmodules, while allowing these to remain available on, e.g., a CD ROM.Thus, for example, a full integrated package could employ a single,“standard” interface which would not require task-switching programs,while avoiding presentation of the full range of features to the user ateach juncture.

This system provides advantages over traditional systems by providing anon-standardized interface with a variable feature set which attainsusability by adapting a subset of the available functionality based onthe context of the data.

EXAMPLE 27

Group Aware Adaptive Computer Interface

The adaptive interface according to the present invention may be used ingroup computing applications. In such a case, the predictivefunctionality is applied to allow the interface to apply rules from onegroup member to a project, even when that group member has notcontributed personally to a particular aspect. This is thus a type ofintelligent agent technology, which, according to the present inventionincludes the characteristics of abstraction and extrapolation, ratherthan rule based analysis which would fail based on divergentcircumstances. This differs from standard rule-based expert systembecause the intelligence applied is not necessarily “expert”, and may beapplied in a relative fashion. Further, extracted user characteristicsneed not completely define a solution to a problem, and indeed, the useof such a technology in group situations presupposes that a contributionof a number of users is desirable, and therefore that the expertise ofany given user is limited.

In order to ensure data integrity after the application or contingentapplication of user characteristics to a datastream, it is desirable totrace the evolution of data structures. This also allows for assistancein the organization and distribution of workgroup responsibilities.Thus, in a workgroup situation, the goal is not optimization ofindividual productivity, but rather optimization of the group result,including all levels of review after an initial phase is complete.

Thus, while an individual user may seek various shortcuts to achievevarious results, the group would benefit by having available allinformation relating to the path taken to achieve that result. Further,the desired result may be modified according to the presumed actions ofthe group, so that the final product is pre-optimized for the group,rather than the individual. Thus, a group member may have his “rules”extracted from his actions, i.e. by neural net backpropagation of errorsprogramming or fuzzy rule definition, to be presented for considerationby another group member. This strategy will allow “better” drafts byconsidering interface. Thus, for example, if it begins raining, theinterface would predict the windshield wipers should be actuated, thewindows and any roof opening closed, and the headlights activated. Thus,the driver could immediately assent to these actions, withoutindividually actuating each control. In such a case, the screeninterface, which may be a heads-up display, would provide a small numberof choices, which may be simply selected. Further, under suchconditions, there would likely be a large amount of mechanical jitterfrom the input device, which would be filtered to ease menu selection.Further, this jitter would indicate an unstable environment condition,which would cause the interface to present an appropriate display. Avoice input may also be used.

EXAMPLE 29

Adaptive Interface Vehicular Control System

An integrated electronics system for an automobile is provided havingcontrol over engine, transmission, traction control, braking,suspension, collision avoidance, climate control, and audio systems.Steering and throttle may also be controlled. Based on driver preferenceand action patterns, the system may optimize the vehicle systems. Forexample, the vehicle may anticipate voluntary or road conditions basedon implicit inputs of the user, thus readying vehicular systems prior tothe actual encounter with certain conditions. Further, a user interfacemay be simplified, based on probable required functionality, thuslimiting required attention by the driver in order to activate aparticular control. By providing such an interface, controls normallyinaccessible may be made accessible, without increasing mechanicalcomplexity, e.g., functions normally controlled by computer may beaccessed through a common user interface, rather than through dedicatedmanual controls.

The automobile control system may also include collision avoidancesystems, which may include imaging sensors and radar or LIDAR rangingand velocity measurement. According to the present invention, a heads-updisplay or simplified graphic user interface in the dashboard or nearthe steering wheel presents predicted options to the driver. Anauxiliary interface may also make certain options available forpassengers.

According to another aspect of the present invention, an automobilepositioning system is provided, which may be extraterrestrial, e.g.,GPS, or terrestrial, e.g., cellular base station, LORAN, etc. Such asystem is described in U.S. Pat. No. 5,390,125, incorporated herein bythe predicted input of a member prior to review by that member. A usermay further tailor the rules for a given project, and “distilled wisdom”from non-group members may also be employed, as in normal expert (AI)systems.

This rule-extraction technology as applied to workgroups is enhanced bythe context sensitivity of the software, where the input of each groupmember may be weighted by considering the context. Again, this techniquemay be used to increase the efficiency of the primary author of asection of a project, as well as better defining the scope ofresponsibility of each member, while still respecting the input of othergroup members.

According to this workgroup rule extraction technology, points ofconflict between group members are highlighted for resolution. As anadjunct to this resolution phase of a project, videoconferencing may beemployed. Further, where a conflict of a similar type had occurred inthe past, data relating to the resolution of that conflict, includingrecorded videoconference, may be retrieved and presented to one or moremembers of the workgroup. In this way, such conflicts may be resolvedbefore it becomes adversarial. Thus, each group member may efficientlyproceed independently, with only major issues requiring meetings and thelike to resolve.

If a workgroup member disagrees with an imputed rule, either explicitly,by review of the rules, or implicitly, by a review of the results, thesystem will allow a review of all decisions influenced by that faultyrule, as well as a proposed correction. This may be addressed by anymember of the group, but usually by the author of the section or thesource of the rule will be the relevant reviewing individual. Rules mayalso be created by the group, rather than from a single individual. Suchrules are more often explicitly defined, rather than derived fromobservation. Such group rules may also be subjected to adaptive forces,especially when overridden frequently.

EXAMPLE 28

Adaptive Interface Vehicular Control System

It is noted that, the adaptive user level interface is of use inuncontrolled environments, such as in a moving vehicle, especially foruse by a driver. An intelligent system of the present invention wouldallow the driver of such a vehicle to execute control sequences, whichmay compensate for the limited ability to interact with an interfacewhile driving. Thus, the driver need not explicitly control allindividual elements, because the driver is assisted by an intelligentreference; see references cited therein. A controller in the automobileis provided with an itinerary for the vehicle travel. Based on positionand itinerary, the vehicle may communicate with various services, suchas food, fuel and lodging providers, to “negotiate” for business. Thedriver may be provided with customized “billboards”, directed to hisdemographics. Reservations and discounts may all be arranged whileen-route. Communication between the automobile and the services ispreferably provided by CDPD services, which is a cellular based 832 MHzband digital data transmission system. Therefore, an existing cell phonesystem or CDPD modem system may be employed for telecommunication.Preferably, a simple display is provided for presentation of commercialmessages to the driver or passenger and for interacting with theservice.

As a matter of practice, the service may be subsidized by the serviceproviders, thus reducing the cost to the consumer. The extent of thesubsidy may be determined by the amount of data transmitted or by theeventual consummation of the transaction negotiated.

Because of the positioning system, any variance from the itinerary maybe transmitted to the service providers, so that reservations may becancelled, or substitute services provided in a different location or ata different time.

The telecommunication system may also be used as an emergency system, tocontact emergency services and/or police in the event of accident ordistress. The transponder system may also be part of an antitheftsystem. The transponder may also be part of a vehicular maintenance anddiagnostic system to ensure proper servicing and to help determine thenature of problems. Raw or processed data may be transmitted to acentralized station for full analysis and diagnosis. Because the vehicleneed not be at the repair shop for diagnosis, problems may be analyzedearlier and based on extensive, objective sensor data.

EXAMPLE 30

Intelligent Internet Appliance

A further application of the present technologies is in a so-called“Internet appliance”. These devices typically are electronic deviceswhich have a concrete function (i.e., do more than merely act as ageneric server) and typically employ at least as a secondary interface,a web browser 3205. In addition, these devices provide a TCP/IP networkconnection and act as a web server, usually for a limited type of data.Therefore, in addition to any real human interface on the device, a webbrowser 3205 may be used as a virtual interface 3304.

According to the present invention, such an Internet Appliance isprovided according to the present invention with advanced features, forexample adaptivity to the user, to the environment, or intelligentalgorithms which learn. In fact, a preferred embodiment provides 3301 arather generic device which serves as a bridge between the Internet, apublic packet switched network 3202 which employs TCP/IP, and a localarea network 3213, for example in a residential, industrial or officeenvironment. The device may further abstract the interface functions fora variety of other devices 3212 as nodes on either the Internet or localarea network 3213, to provide a common control system and interface.

A preferred embodiment also encompasses certain other features which maybe used as resources for the networked devices or as usable features ofthe device.

The Internet, or other wide area network, may be connected in any knownmanner, for example, X.25/ISDN D-channel, dial-up over POTS (e.g., v.34,v.90, v.91), ISDN, xDSL, ADSL, cable modem, frame relay, TI line, ATM,or other communications system. Typically, a system is provided witheither a commonly used access method, such as v.90 or ISDN, or areplaceable communications module with a generic interface. Such systemsare well known.

The local area network 3213 is also well known, and may include, forexample, as a physical layer, 10 Base T, 100 Base T, HomeRun (Cat. 3twisted pair/telephone twisted pair/power line transmission, from IntelCorp., e.g., Intel 21145 device/Tut systems), Universal Serial Bus(USB), Firewire (IEEE-1394), optical fiber, or other known computernetwork. The protocol may be, for example, TCP/IP, IPX, ATM, USB,IEEE-1394, or other known or proprietary appropriate communicationsprotocol.

While not required, a particular aspect of a preferred embodimentaccording to the present invention is the ability to interface “dumb”devices as nodes on the LAN 3213 with an intelligent device 3201, whileallowing the user to interact primarily with the intelligent device3201. This scheme therefore reduces redundancy and increasesfunctionality.

Therefore, in an exemplary embodiment, an intelligent home isestablished, with most or all electrical appliances 3223 and electronicdevices interfaced with the system, for example through theaforementioned Homerun system, using any of the supported physicallayers. Each device is provided as a relatively simple control, forexample, remotely controllable (or where applicable, dimmable) lights3224, control over normal use and peak electrical demand of heavyappliances 3223, as well as inter-device communications for consumerelectronics 3221. Therefore, the intelligent device acts as an externalcommunications and control node for the entire network, and may, forexample, control telephony 3214 functions in addition.

Exemplary devices to be controlled in a home include householdappliances 3223, HVAC 3215, alarm systems 3217, consumer electronics3221, and the like, and/or provide for communications purposes. An alarmsystem 3217 embodiment, for example, may employ a video camera input3219 for capture and analysis of images, as well as motion orirregularity detection. The intelligent device 3201 may, for example,employ neural networks or other intelligent analysis technology foranalyzing data patterns indicative of particular states. An alarm outputmay be produced, for example, through standard alarms, as well asthrough a telephone 3214 interface of the system.

The system may therefore set/control/monitor the status of anyhome-based device—oven, stove, alarm, washing machine, dryer, iron,lights, computer, oil/gas burner, thermostat IS 3222, location ofautomobiles 3218, camera, pump 3226 (pool, sump), sprinkler 3225,stereo/video systems, home surveillance system 3216. This may beespecially important if the user is away from home for an extendedperiod of time, or if he or she wants to change the schedule ofsomething, or travel plans change. For a home surveillance system 3216,pattern recognition may be employed to monitor all sensors, includingcameras, to detect abnormal patterns or changes in condition.

Thus, since the intelligent device incorporates a web server, thephysical proximity of the user is not critical for interaction with thedevice, and all devices on the LAN 3213 may be controlled remotely,automatically, and in synchrony.

In one embodiment, the intelligent device includes a videoconferencing3220/video capture system, including any or all known features for suchsystems, for example as described in the background of the invention.Therefore, in addition to a base level of functionality, such anembodiment would also likely include (a) telephony 3214 interface, (b)video capture, (c) video codec, (d) audio capture, (e) audio codec, (f)full duplex speakerphone, (g) video output, and (h) audio output.

In another embodiment, a speech interface is provided for interpretinghuman speech as an input and/or producing synthesized speech as anoutput. Therefore, such a device would include speech recognition and/orsynthesis technologies, as well as a semantic data processor.

Preferably, the device allows use of a simplified web browser interface3205, such as which may be supported by personal digital assistants(PDAs) and enhanced digital data cellular telephones, e.g., handhelddevice markup language (HDML). This, for example, allows a remote userto communicate through wireless networks 3211 or the like, and thereforeavoids the need for a full personal computer as a human interface.

Advantageously, the device may be interfaced with a telephone 3214communication system, allowing use as a voice and/or video messagerecorder, and allowing remote access to the stored information, eitherthrough a dialup connection and/or through the network. In this case,the intelligent device 3201 may act as a computer telephony interface,and all communications devices logically under this device act as “netphones”, i.e., voice communications devices which communicate over datanetworks. Therefore, all telephony control and computer telephonyfunctions may be integrated into the device, for example, voice mail,auto-attendant, call center, and the like. Further, the Internetinterface allows remote messaging and control over the telephony system,as well as virtual networking, Internet telephony, paging functions, andvoice and data integration.

The intelligent device 3201 may also interface with various mediaelectronics devices, and for example, may act as a “rights server” 3208or other aspect of a copyright protection and royaltycollection/enforcement system 3307. Typically, these functions entaile-commerce functions, and may require X.22 and/or XML communications andtranslations. In addition, such functions also typically involveencryption/decryption 3207, as well as key management, which are alsopreferably supported by the device. Such support may be in hardware orsoftware.

Another aspect of the invention provides an index and/or catalogdatabase 3204 for media information 3209 or media metadata 3210information. Thus, data relating to a VCR tape or other recorded mediamay be subjected to search criteria without requiring access orcontemporaneous analysis of the media content itself. Therefore, apreferred embodiment of the intelligent device includes mass storage andretrieval capability 3204, for example, magnetic disk, RW-CD, or RW-DVD.This mass storage and retrieval capability 3204 may be used, not onlyfor databases, but also for computer software, media and content storageand retrieval 3303. Thus, the device may also serve as a video datarecorder, capturing video data and storing it digitally, for example,employing the aforementioned video and audio codecs. In this case, it ispreferable that the intelligent device 3201 also include a direct mediaaccess port 3203, for example a broadcast TV tuner, ATSC/HDTV tuner,cable tuner, DVD reader, CD reader, satellite video decoder, NTSCcomposite/S-VHS, and/or other type of media content information input3302. With such storage, the intelligent device 3201 may also assume thestandard functions of computer network servers, for example, fileserving, print serving, fax serving, application serving, client/serverapplication support, as well as traditional networking functions, suchas bridging, routing, switching, virtual private network, voice-over-IP,firewall functions, remote access serving, and the like. It should alsobe apparent that the intelligent device 3201 may also serve as apersonal computer 3206 itself, and thus does not require additionalsystems for basic functionality.

In a media recording system embodiment, the system preferably notifiesthe user if the “program”, i.e., instructions, are incomplete,ambiguous, or impossible to complete. For example, if a single channelselector is provided, no more than one channel may be monitored at atime. Further, where irreversible actions are necessary, the user ispreferably informed and allowed to make a choice, for example, if lackof storage space forces a choice to be made between new and archivalmaterial. A conflict management system is provided which arbitratesbetween the conflicting demands, for example if a second user isprogramming the same device (for example, the VCR) to record a show atthe same time.

Thus, it is apparent that the intelligent device 3201 according to thisembodiment of the present invention may incorporate many differentfunctions, some of which are defined purely by software and processingavailability, and others by particular hardware devices for performingspecific functions.

Another aspect of the invention defines a special training mode of theintelligent device, which allows the user to improve the functionalityof the system by ensuring that any intelligence algorithms willcorrectly operate in an anticipated and/or desired manner. In this mode,responses of the user are provoked which indicate user preferences,preferably in a manner which resolves ambiguities encountered with priordata sets. Thus, where the system identifies a situation where adecision is difficult, e.g., where the data analysis does not output anyselected actions which will likely correspond to the user desires orpreferences, or where ex post facto the user indicates that aninappropriate choice was made, the particular data structures may bestored and abstracted for later presentation to the user. In this case,such structures are presented by the system to the user, during atraining session, to train the system relating to the desired responseto particular data environments. In this way, the user is notnecessarily burdened with training tasks during normal use of thedevice, and opportunities for such training are not lost. Where thesystem is untrained, and an “intelligent” response or mode of operationcannot be resolved, a default mode of operation may be defined. Further,such a default mode is preferably always available, at the request ofthe user, thus allowing use where an adaptive system is undesired ordifficult to employ.

In a television application, the Internet appliance preferably hasaccess to an electronic program guide (EPG). Such EPG systems are known,and typically provide an efficient staring point for user programming.These EPG may be provided as an embedded signal in a broadcast stream,through a dial-up network, through the internet, or on distributionmedia, such as CD-ROM, OCR scanning of TV-Guide (or the like) or otherknown means. EPGs contain a concise semantic description of programcontent, which typically is both sufficient for user evaluation, andbrief enough for rapid evaluation. The system may therefore analyze userpreferences in this semantic space and provide adaptive presentation ofelements of the EPG to the user. Of course, a media data stream analysisembodiment of the invention, as disclosed above, may be used inconjunction with or in lieu of the EPG system.

The system preferably maintains an updated index of available data.Thus, newly acquired data is added to the index, and deleted data ispurged from the index. The system preferably compares new data topreviously encountered data, to avoid redundant processing. For example,the system preferably recognizes events/programs that have previouslybeen recorded, and checks to determine whether they are still in theindex. In this context, the user is preferably provided with low-levelfile maintenance tools, for example to manually control the addition ordeletion of data, which is then correctly represented in the index.

Because the Internet appliance is connected to the Internet, so-calledmulticasts may be monitored for correspondence with user preferences.Therefore, it is understood that the operation of the present inventionis not limited to traditional television broadcasts, and that streamingvideo and audio, as well as stored images, sound files (e.g., MIDI, MP3,A2B, RealAudio), text, and multimedia streams may be analyzed based onthe adaptive principles presented herein 3305.

The system may also integrate Internet data with other types of data,for example providing access to stored or static data corresponding to adata stream. The retrieval and storage of such data may also beadaptively controlled in accordance with the present invention. Thus, itis expressly understood that the intelligent device may act as a “VCR”(albeit not necessarily employing a known type of videocassette tape),to record media 3306.

The Internet appliance may also operate autonomously, capturing datawhich corresponds to user preferences and profiles, thus reducinglatency for the user, and potentially shifting data transfers tooff-peak periods. Such a system operates in this mode as a so-called“agent” system. Likewise, the device may also be linked to otherintelligent devices, to provide an intelligent interaction therebetween.

The preferred user interface maintains user levels constant over longperiods, i.e., not rapidly adaptive, to allow for quick accessing over alow bandwidth connection, such as a telephone, or using succinctdisplays, such as might be found on a personal digital assistant. Thus,the user can rely on memory of the interface functionality and layout toreduce data transmissions and reduce search time. In one embodiment, theinterface may be “forced” to a particular type, as either a permanentinterface, or as a starting point for adaptivity. Thus, the user may beprovided with an interface design mode of operation.

The user interaction with each “device”, which may be real or virtual(implemented as a software construct in a relatively general purposecomputer), is preferably carefully designed for each device. A commonuser interface paradigm is preferably provided for correspondingfunctions, while the user interface is preferably optimized for dealingwith the specific functions of each particular device. Thus, a similaruser interface and screen layout is employed for functions that are thesame across a variety of devices. In this regard, it is an aspect of anembodiment of the invention to translate user interface systems, even ina high level state, to other forms. Thus, in a multi-brand environment,related components may have native interfaces that are both welldeveloped and distinctly different. Therefore, the present inventionallows for a translation or remapping of the functionality into a commonparadigm. Where aspects cannot be adequately translated, the nativeinterface may be presented to the user.

It should be understood that the preferred embodiments and examplesdescribed herein are for illustrative purposes only and are not to beconstrued as limiting the scope of the present invention, which isproperly delineated only in the appended claims.

REFERENCES

“32-bit Floating-Point DSP Processors”, EDN, Nov. 7, 1991, pp. 127-146.

“A New Class of Markov Processes for Image Encoding”, School ofMathematics, Georgia Inst. of Technology (1988), pp. 14-32.

“A show and tell of the QBIC technology—Query By Image Content (QBIC)”,IBM QBIC Almaden web site, pp. 1-4.

“ABI WHAP, Web Hypertext Applications Processor,”http://alphabase.com/abi3/whapinfo.html#profiling, (Jul. 11, 1996).

“AdForce Feature Set”, http://www.imgis.com/index.html/core/p2—2html(Apr. 11, 1997).

“Bar Code Programs VCR”, Design News, Feb. 1, 1988, 26.

“C-Cube CL550 JPEG Image Compression Processor”, Preliminary Data Book,August 1991, and addendum dated Nov. 20, 1991.

“Chaos & Non-Linear Models in Economics”.

“Chaos Theory in the Financial Markets. Applying Fractals, Fuzzy Logic,Genetic Algorithms”.

“Construction of Fractal Objects with Iterated Function Systems”,Siggraph '85 Proceedings, 19(3):271-278 (1985).

“Data Compression: Pntng by Numbrs”, The Economist, May 21, 1988.

“EMC² Pushes Video Rental By Satellite”, Electronic Engineering Times,Dec. 2, 1991, p. 1, p. 98.

“Evolutionary Economics & Chaos Theory”.

“Finger Painting”, Information Display 12, p. 18, 1981.

“Four Eyes”, MIT Media Lab web site; pp. 1-2.

“Fractal Geometry-Understanding Chaos”, Georgia Tech Alumni Magazine, p.16 (Spring 1986).

“Fractal Modelling of Biological Structures”, Perspectives in BiologicalDynamics and Theoretical Medicine, Koslow, Mandell, Shlesinger, eds.,Annals of New York Academy of Sciences, vol. 504, 179-194 (dateunknown).

“Fractal Modelling of Real World Images, Lecture Notes for Fractals:Introduction, Basics and Perspectives”, Siggraph (1987).

“Fractals Yield High Compression”; Electronic Engineering Times; Sep.30, 1991; p. 39.

“Fractals-A Geometry of Nature”, Georgia Institute of TechnologyResearch Horizons; p. 9 (Spring 1986).

“Frequently asked questions about visual information retrieval”, VirageIncorporated web site; pp. 1-3.

“How to find the best value in VCRs”, Consumer Reports, March 1988,135-141.

“IBM Ultimedia Manager 1.1 and Clinet Search”, IBM software web site,pp. 1-4.

“Image Compression Using Fractals and Wavelets”, Final Report for thePhase II Contract Sponsored by the Office of Naval Research, ContractNo. N00014-91-C-0117, Netrologic Inc., San Diego, Calif. (Jun. 2, 1993).

“Image Detection and Registration”, Digital Image Processing, Pratt,Wiley, N.Y., 1991.

“IPRO,” http://www.ipro.com/, Internet profiles Corporation Home andother Web Pages (Jul. 11, 1996).

“Jacob Methodology” @http://WWCSAI.diepa.unipa.it/researchprojects/jacob/jacob-method.html.

“Low-Cost VCRs: More For Less”, Consumer Reports, March 1990, 168-172.

“Machine Now Reads, enters Information 25 Times Faster Than HumanKeyboard Operators”, Information Display 9, p. 18 (1981).

“Market Analysis. Applying Chaos Theory to Investment & Economics”.

“Media Planning is Redefined in a New Era of Online Advertising,” PRNewswire, (Feb. 5, 1996).

“MPEG: A Video Compression Standard for Multimedia Applications”, LeGall, Communications of the ACM, vol. 34, No. 4, April 1991, pp. 47-58.

“My Yahoo! news summary for My Yahoo! Quotes”, http://my.yahoo.con,(Jan. 27, 1997).

“NetGravity Announces Adserver 2.1”,http://www.netgravity.com/news/pressrel/launch21.html (Apr. 11, 1997).

“Netscape & NetGravity: Any Questions?”, http://www.netgravity.com/,(Jul. 11, 1996).

“Network Site Main”,http://www.doubleclick.net/frames/general/nets2set.htm (Apr. 11, 1997).

“New Beetle Cursor Director Escapes All Surface Constraints”,Information Display 10, p. 12, 1984.

“Nielsen Views VCRs”, Television Digest, Jun. 23, 1988, 15.

“Photobook”, MIT Media Lab web site; Aug. 7, 1996; pp. 1-2.

“Profiting from Chaos. Using Chaos Theory for Market Timing, StockSelection & Option”.

“Real Media,” http://www.realmedia.com/index.html, (Jul. 11, 1996).

“Scanner Converts Materials to Electronic Files for PCs”, IEEE CG&A,December 1984, p. 76.

“Shape Analysis”, Digital Image Processing, Pratt, Wiley, N.Y., 1991.

“The Front Page”, http://live.excite.com/?aBb (Jan. 27, 1997) and (Apr.11, 1997).

“The Highs and Lows of Nielsen Homevideo Index”, Marketing & MediaDecisions, November 1985, 84-86+.

“The Pointcast Network,” http:/www.pointcast.com/, (1996, Spring).

“The Power of PenPoint”, Can et al., 1991, p. 39, Chapter 13, pp.258-260.

“The QBIC Project”, IBM QBIC Almaden web site, home page.

“The Quest for ‘User Friendly’”, U.S. News & World Report, Jun. 13,1988. 54-56.

“The Smart House: Human Factors in Home Automation”, Human Factors inPractice, December 1990, 1-36.

“VCR, Camcorder Trends”, Television Digest, Vol. 29, Mar. 20, 1989, 16.

“VCR's: A Look At The Top Of The Line”, Consumer Reports, March 1989,167-170.

“VHS Videocassette Recorders”, Consumer Guide, 1990, 17-20.

“Virage—Visual Information Retrieval”, Virage Incorporated, home page.

“Virage Products”, Virage Incorporated web site; pp. 1-2.

“Visual Information Retrieval: A Virage Perspective Revision 3”, VirageIncorporated web site; 1995; pp. 1-13.

“Visual Pattern Recognition by Moment Invariants”, IRE Trans. Inform.Theory, vol. 8, Februrary 1962, pp. 179-187.

“Voice Recognition and Speech Processing”, Elektor Electronics,September 1985, pp. 56-57.

“Welcome to Lycos,” http://www.lycos.com, (Jan. 27, 1997).

“Workshop Report: NSF—ARPA Workshop on Visual Information ManagementSystems”, Virage Incorporated web. site; pp. 1-15.

“WWW.amazon.com”.

“WWW.firefly.com”.

Abadi, M., et al, “Authentication and Delegation with Smart-cards”, Oct.22, 1990, revised Jul. 30, 1992 Report 67, Systems Research Center,Digital Equipment Corp., Palo Alto, Calif.

Abatemarco, Fred, “From the Editor”, Popular Science, September 1992, p.4 Abe, S., Y. Tonomura, Systems and Computers in Japan, vol. 24, No. 7,“Scene Retrieval Method Using Temporal Condition Changes”, pp. 92-101,1993.

Abedini, Kamran, “An Ergonomically-improved Remote Control Unit Design”,Interface '87 Proceedings, 375-380.

Abedini, Kamran, and Hadad, George, “Guidelines For Designing BetterVCRs”, Report No. IME 462, Feb. 4, 1987.

Advertisement for “TV Decision,“CableVision, Aug. 4, 1986.

Aleksander, I., “Guide to Pattern Recognition Using Random-AccessMemories”, Computers and Digital Techniques, 2(1):29-40 (February 1979).

American National Standard, “Financial Institution Retail MessageAuthentication”, ANSI X9.19 1986.

American National Standard, “Interchange Message Specification for Debitand Credit Card Message Exchange Among Financial Institutions”, ANSIX9.2-1988.

Anderson, F., W. Christiansen, B. Kortegaard, “Real Time, Video ImageCentroid Tracker”, Apr. 16-20, 1990.

Anderson, Ross J., “UEPS—A Second Generation Electronic Wallet”, Proc.of the Second European Symposium on Research in Computer Security(ESORICS), Touluse, France, pp. 411-418, Touluse, France.

Anderson, Ross, “Why Cryptosystems Fail”, Proc. 1st Conf. Computer andComm. Security, pp. 215-227, November 1993.

Anson, L., “Fractal Image Compression”, Byte, October 1993, pp. 195-202;“Fractal Compression Goes On-Line”, Byte, September 1993.

Anson, L., M. Barnsley; “Graphics Compression Technology”; SunWorld; pp.43-52 (October 1991).

Antonofs, M., “Stay Tuned for Smart TV,” Popular Science, November 1990,pp. 62-65.

Appriou, A., “Interet des theories de l'incertain en fusion de donnees”,Colloque International sur le Radar Paris, 24-28 avril 1989.

Appriou, A., “Procedure d'aide a la decision multi-informateurs.Applications a la classification multi-capteurs de cibles”, Symposium del'Avionics Panel (AGARD) Turquie, 25-29 avril 1988.

Arman et al., “Feature Management for Large Video Databases”, 1993.(Abstract Only).

Arman et al., “Image Processing on Compressed Data for Large VideoDatabases”, Proc. of First ACM Int. Conf. on Multimedia, Anaheim,Calif., 1-6 Aug. 1993, pp. 267-272.

Arman et al., “Image Processing on Encoded Video Sequences”, ACMMultimedia Systems Journal, to appear 1994.

Arndt, T., “A Survey of Recent Research in Image Database Management”,IEEE Publication No. TH0330-1/90/0000/0092, pp. 92-97, 1990.

Arrow, K. J., “Social choice and individual valves”, John Wiley and SonsInc. (1963).

Arrowsmith, D K & C M Place: “An Introduction to Dynamical Systems”,Cambridge University Press, Cambridge, 1990.

Asian Technology Information Program (ATIP) Report: ATIP95.65: HumanComputer Interface International, 7/95 Yokohama.

Astrom, K. J., and B. Wittenmark, “Adaptive Control”, Addison-WesleyPublishing Company (1989) pp. 105-215.

Astrom, K. J., T. Hagglund, “Automatic Tuning of PID Controllers”,Instrument Society of America, Research Triangle Park, N.C. (1988) pp.105-132.

Atkinson, Terry, “VCR Programming: Making Life Easier Using Bar Codes”.

Bach, J. R., C. Fuller, A. Gupta, A. Hampapur, B. Horowitz, R. Humphrey,R. C. Jain, and C. Shu. Virage image search engine: an open frameworkfor image management. In Symposium on Electronic Imaging: Science andTechnology—Storage & Retrieval for Image and Video Databases IV, pages76-87. IS&T/SPIE, 1996.

Bagley, H. & Sloan, J., “Optical Processing: Ready For Machine Vision?”,Photonics Spectra, August 1993, pp. 101-106.

Bains, S., “Trained Neural Network Recognizes Faces”, Laser Focus World,June, 1993, pp. 26-28.

Baker, Gregory L., & Jerry P Gollub: “Chaotic Dynamics: AnIntroduction”, Cambridge University Press, Cambridge, 1990.

Baldwin, William, “Just the Bare Facts, Please”, Forbes Magazine, Dec.12, 1988.

Ballard, D. H., and Brown, C. M., Computer Vision, Prentice Hall,Englewood Cliffs, N.J. (1982); Optical Engineering 28:5 (May1988)(Special Issue on product inspection).

Barber et al. “Ultimedia Manager: Query by Image Content and it'sApplications” IEE, 1994, pp. 424-429, January 1994.

Barnsley et al., “A Better Way to Compress Images”, Byte, January 1988,pp. 213-225.

Barnsley et al., “Chaotic Compression”, Computer Graphics World,November 1987.

Barnsley et al., “Harnessing Chaos For Images Systhesis”, ComputerGraphics, 22(4):131-140 (August, 1988).

Barnsley et al., “Hidden Variable Fractal Interpolation Functions”,School of Mathematics, Georgia Institute of Technology, Atlanta, Ga.30332, July, 1986.

Barnsley, M., L. Anson, “Graphics Compression Technology, SunWorld,October 1991, pp. 42-52.

Barnsley, M. F., A. Jacquin, F. Malassenet, L. Reuter & A. D. Sloan,‘Harnessing chaos for image synthesis’, Computer Graphics, vol 22 no 4pp 131-140, (August, 1988).

Barnsley, M. F. , A. E. Jacquin, ‘Application of recurrent iteratedfunction systems to images’, Visual Comm. and Image Processing, volSPIE-1001, 1988.

Barnsley, M. F., “Fractals Everywhere”, Academic Press, Boston, Mass.,1988,.

Barnsley, M. F., and Demko, S., “Iterated Function Systems and TheGlobal Construction of Fractals”, Proc. R. Soc. Lond., A399:243-275(1985).

Barnsley, M. F., Ervin, V., Hardin, D., Lancaster, J., “Solution of anInverse Problem for Fractals and Other Sets”, Proc. Natl. Acad. Sci.U.S.A., 83:1975-1977 (April 1986).

Barros, et al. “Indexing Multispectral Images for Content-BasedRetrieval”, Proc. 23rd AIPR Workshop on Image and Information Retrieval,Proc. 23rd Workshop, Washington, D.C., October 1994, pp. 25-36.

Batchelor, B. G., “Pattern Recognition, Ideas in Practice”, PlenumPress, London and New York, (1978).

Batchelor, B. G., “Practical Approach to Pattern Classification”, PlenumPress, London and New York, (1974).

Baxes, Gregory A., “Digital Signal Processing, A Practical Primer”,Prentice-Hall, Englewood Cliffs, N.J. (1984).

Beaumont J M, “Image data compression using fractal techniques”, BritishTelecom Technological Journal 9(4):93-108 (1991).

Belkin, N. J., Croft, W. B., “Information Filtering And InformationRetrieval: Two Sides of the Same Coin?”, Communications of the ACM,December 1992, vol. 35, No. 12, pp. 29-38.

Bellman, R. E., L. A. Zadeh, “Decision making in a fuzzy environment”,Management Science, 17(4) (December 1970).

Bender, M., “EFTS: Electronic Funds Transfer Systems”, Kennikat Press,Port Washington, N.Y., pp. 43-46 1975.

Bensch, U., “VPV—VIDEOTEXT PROGRAMS VIDEORECORDER”, IEEE Transactions onConsumer Electronics, Vol. 34, No. 3, 788-792 (1988),.

Berger, Ivan, “Secrets of the Universals”, Video, February 1989, 45-47+.

Beringer, D. B., “A Comparative Evaluation of Calculator Watch DataEntry Technologies: Keyboards to Chalkboards”, Applied Ergonomics,December 1985, 275-278.

Berniker, M., “Nielsen plans Internet Service,” Broadcasting & Cable,125(30):34 (Jul. 24, 1995).

Berry, Deanne, et al. In an Apr. 10, 1990 news release, Symantecannounced a new version of MORE (TM).

Berry, Jonathan, “A Potent New Tool for Selling Database Marketing”,Business Week, Sep. 5, 1994, pp. 34-40.

Berry, M V, I C Persival & N O Weiss: “Dynamical Chaos”, The RoyalSociety, London, 1987, Proceedings of a Royal Society Discussion Meetingheld on 4 & 5 Feb. 1987.

Bestler, Caitlin: Flexible Data Structures and Interface Rituals ForRapid Development of OSD Applications; 93 NCTA Tech. Papers; Jun. 6,1993; pp. 223-236.

Betts, M., “Sentry cuts access to naughty bits,” Computers and Security,vol. 14, No. 7, p. 615 (1995).

Bhatnagar, R. K., L. N. Kamal, “Handling uncertain information: a reviewof numeric and non-numeric methods”, Uncertainty in ArtificialIntelligence, L. N. Kamal and J. F. Lemmer, Eds. (1986).

Bier, E. A. et al. “MMM: A User Interface Architecture for SharedEditors on a Single Screen,” Proceedings of the ACM Symposium on UserInterface Software and Technology, Nov. 11-13, 1991, p. 79.

Bimbo et al., “Sequence Retrieval by Contents through Spatio TemporalIndexing”, IEEE on CD-ROM, pp. 88-92, Aug. 24, 1993.

Bimbo, A. D., et al, “3-D Visual Query Language for Image Databases”,Journal Of Visual Languages & Computing, 1992, pp. 257-271.

Binaghi, E., et al, “Indexing and Fuzzy Logic Based Retrieval of ColorImages”, Visual Database Systems, II, 1992, pp. 79-92.

Binaghi, E., et al., “A Knowledge-Based Environment for Assessment ofColor Similarity”, Proc. 2nd Annual Conference on Topics for A1, pp.268-285 (1990).

Bishop, Edward W., and Guinness, G. Victor Jr., “Human FactorsInteraction with Industrial Design”, Human Factors, 8(4):279-289 (August1966).

Blair, D., R. Pollack, “La logique du choix collectif” Pour la Science(1983).

Bolot, J.; Turletti, T. & Wakeman, I.; “Scalable Feedback Control forMulticast Video Distribution In the Internet”, Computer CommunicationReview, vol. 24, No. 4 October 1994, Proceedings of SIGCOMM 94, pp.58-67.

Bos et al., “SmartCash: a Practical Electronic Payment System”, pp. 1-8;August 1990.

Boy, Guy A., Intelligent Assistant Systems, Harcourt Brace Jovanovich,1991, uses the term “Intelligent Assistant Systems”.

Bristol, E. H., & T. W. Kraus, “Life with Pattern Adaptation”,Proceedings 1984 American Control Conference, pp. 888-892, San Diego,Calif. (1984).

Brown, Edward, “Human Factors Concepts For Management”, Proceedings ofthe Human Factors Society, 1973, 372-375.

Brown, Robert: “Statistical Forecasting for Inventory Control”,McGraw-Hill Book Co., New York, 1958.

Bruce, J W, & P J Giblin: “Curves and Singularities”, CambridgeUniversity Press, Cambridge, 1992.

Brugliera, Vito, “Digital On-Screen Display--A New Technology for theConsumer Interface”, Symposium Record Cable Sessions. Jun. 11, 1993, pp.571-586.

Bulkeley, Debra, “The Smartest House in America”, Design News, Oct. 19,1987, 56-61.

Burk et al, “Value Exchange Systems Enabling Security andUnobservability”, Computers & Security, 9 1990, pp. 715-721.

Burr, D. J., “A Neural Network Digit Recognizer”, Proceedings of the1986 IEEE International Conference of Systems, Man and Cybernetics,Atlanta, Ga., pp. 1621-1625.

Bursky, D., “Improved DSP ICs Eye New Horizons”, Electronic Design, Nov.11, 1993, pp. 69-82.

Bussey, H. E., et al., “Service Architecture, Prototype Description, andNetwork Implications of a Personalized Information Grazing Service,”IEEE Multiple Facets of Integration Conference Proceedings, vol. 3, No.Conf. 9, Jun. 3, 1990, pp. 1046-1053.

Byte Magazine, January 1988.

Caffery, B., “Fractal Compression Breakthrough for MultimediaApplications”, Inside, Oct. 9, 1991.

Card, Stuart K., “A Method for Calculating Performance times for Usersof Interactive Computing Systems”, IEEE, 1979, 653-658.

Carlson, Mark A., “Design Goals for an Effective User Interface”, HumanInterfacing with Instruments, Electro/82 Proceedings, 3/1/1-3/1/4.

Carpenter, G. A., S. Grossberg, “The Art of Adaptive Pattern Recognitionby a Self-Organizing Neural Network”, IEEE Computer, March 1988, pp.77-88.

Carroll, Paul B., “High Tech Gear Draws Cries of “Uncle”, Wall StreetJournal, Apr. 27, 1988, 29.

Casasent, D., and Tescher, A., Eds., “Hybrid Image and Signal ProcessingII”, Proc. SPIE Technical Symposium, April 1990, Orlando Fla. 1297(1990).

Casasent, D., et al., “General I and Q Data Processing on a MultichannelAO System”, Applied Optics, 25(18):3217-24 (Sep. 15, 1986).

Casasent, D., Photonics Spectra, November 1991, pp. 134-140.

Casdagli, Martin, & Stephen Eubank: “Nonlinear Modelling andForecasting”, Addison-Wesley Publishing Co., Redwood City, 1992.

Case Study: The CIRRUS Banking Network, Comm. ACM 8, 28 pp. 7970-8078,August 1985.

Caudill, M., “Neural Networks Primer-Part III”, AI Expert, June 1988,pp. 53-59.

Cawkell, A. E., “Current Activities in Image Processing Part III:Indexing Image Collections”, CRITique, vol. 4, No. 8, May 1992, pp.1-11, ALSIB, London.

Chalmers, M., Chitson, P., “Bead: Explorations In InformationVisualization”, 15th Ann. Int'l SIGIR 92/Denmark-June 1992, pp. 330-337.

Chang et al., “Image Information Systems: Where Do We Go From Here?”,IEEE Transactions on Knowledge and Data Engineering, vol. 4, No. 5,October 1992, pp. 431-442.

Chang et al., “Intelligent Database Retrieval by Visual Reasoning”, PROCFourteenth Annual International Computer Software and ApplicationConference, 31 Oct.-1 Nov. 1990, pp. 459-464.

Chang, C., “Retrieving the Most Similar Symbolic Pictures from PictorialDatabases”, Information Processing & Management, vol. 28, No. 5, 1992.

Chang, C., et al, “Retrieval of Similar Pictures on PictorialDatabases”, Pattern Recognition, vol. 24, No. 7, 1991, pp. 675-680.

Chang, N. S., et al., “Picture Query Languages for Pictorial Data-BaseSystems”, Computer vol. 14, No. 11, pp. 23-33 (November 1981).

Chang, N. S., et al., “Query-by-Pictorial Example”, IEEE Transactions onSoftware Engineering, vol. SE-6, No. 6, pp. 519-524 (November 1980).

Chang, S., et al, “An Intelligent Image Database System”, IEEETransactions On Software Engineering, vol. 14, No. 5, May 1988, pp.681-688.

Chang, S.-F, Compressed-domain techniques for image/video indexing andmanipulation. In Proceedings, I.E.E.E. International Conference on ImageProcessing, Washington, D.C., Oct. 1995. invited paper to the specialsession on Digital Library and Video on Demand.

Chang, S.-K., Principles of Pictorial Information Systems Design.Prentice Hall, 1989.

Chang, S.-K., Q. Y. Shi, and C. Y. Yan. “Iconic indexing by 2-Dstrings”. IEEE Trans. On Pattern Analysis And Machine Intelligence, vol.9, No. 3, May 1987, pp. 413-428.

Chang, Yuh-Lin, Zeng, Wenjun, Kamel, Ibrahim, Alonso, Rafael,“Integrated Image and Speech Analysis for Content-Based Video Indexing”.

Chao, J. J., E. Drakopoulos, C. C. Lee, “An evidential reasoningapproach to distributed multiple hypothesis detection”, Proceedings ofthe 20th Conference on decision and control, Los Angeles, Calif.,December 1987.

Chao, T.-H.; Hegblom, E.; Lau, B.; Stoner, W. W.; Miceli, W. J.,“Optoelectronically implemented neural network with a waveletpreprocessor”, Proceedings of the SPIE—The International Society forOptical Engineering, 2026:472-82(1993).

Chapra, Steven C, & Raymond P Canale: “Numerical Methods for Engineers”,McGraw-Hill Book Co., New York, 1988.

Charles, S., et al, “Using Depictive Queries to Search PictorialDatabases”, Human Computer Interaction, 1990, pp. 493-498.

Chassery, J. M., et al., “An Interactive Segmentation Method Based onContextual Color and Shape Criterion”, IEEE Transactions on PatternAnalysis and Machine Intelligence, vol. PAMI-6, No. 6, (November 1984).

Chaum et al, “Untraceable Electronic Cash”, Advances in Cryptology,1988, pp. 319-327.

Chaum et al; “Achieving Electronic Privacy”, Scientific American, pp.319-327; 1988.

Chaum, D. “Security without Identification: Card Computers to Make BigBrother Obsolete”, Communications of the ACM, 28(10), October 1985, pp.1030-1044.

Chaum, D. “Untraceable Electronic Mail, Return Addresses, and DigitalPseudonyms”, Communications of the ACM, vol. 24, No. 2, February, 1981.

Chaum, D., “Achieving Electronic Privacy”, Scientific American, August1992, pp. 96-101.

Chaum, D.L. et al.; “Implementing Capability-Based Protection UsingEncryption”; Electronics Research Laboratory, College of Engineering,University of California, Berkeley, Calif.; Jul. 17, 1978.

Chen et al., “Adaptive Coding of Monochrome and Color Images”, November1977, pp. 1285-1292.

Chen, Z., et al, “Computer Vision for Robust 3D Aircraft Recognitionwith Fast Library Search”, Pattern Recognition, vol. 24, No. 5, pp.375-390, 1991, printed in Great Britain.

Cheong, C. K.; Aizawa, K.; Saito, T.; Hatori, M., “Adaptive edgedetection with fractal dimension”, Transactions of the Institute ofElectronics Information and Communication Engineers D-II,J76D-II(11):2459-63 (1993).

Child, Jeff, “H.324 Paves Road For Mainstream Video Telephony”, ComputerDesign, January 1997, pp. 107-110.

Chua, T.-S., S.-K. Lim, and H.-K. Pung. Content-based retrieval ofsegmented images. In Proc. ACM Intern. Conf. Multimedia, October 1994.

Cobb, Nathan, “I don't get it”, Boston Sunday Globe Magazine, Mar. 25,1990, 23-29.

Cohen, Danny; “Computerized Commerce”; ISI Reprint Series ISI/RS-89/243;October, 1989; Reprinted from Information Processing 89, Proceedings ofthe IFIP World Computer Congress, held Aug. 28-Sep. 1, 1989.

Cohen, Danny; “Electronic Commerce”; University of Southern California,Information Sciences Institute, Research Report ISI/RR-89-244; October,1989.

Cohen, R., “FullPixelSearch Helps Users Locate Graphics”, MacWeek, Aug.23, 1993, p. 77.

Commaford, C., “User-Resonsive Software Must Anticipate Our Needs”, PCWeek, May 24, 1993.

Common European Newsletter, Multimedia Content manipulation andManagement, http://ww.esat.kuleuven.ac.be/˜konijin/ . . .

CompuServe Information Service Users Guide, CompuServe International,1986, pp. 109-114.

Computer Shopper, November 1994, “Internet for Profit”, pp. 180-182,187, 190-192, 522-528, 532, 534.

Computer Visions, Graphics, and Image Processing 1987, 37:54-115.

Computer, Vol. 28(9), September 1995.

Computers and Biomedical Research 5, 388-410 (1972).

Compuvid Sales Manual (date unknown).

Consumer Digest advertisement: Xpand Your TV's Capability: Fall/Winter1992; p. 215.

Cooper, L. N., “A Possible Organization of Animal Memory and Learning”,Nobel 24, (1973), Collective Properties of Physical Systems, pp.252-264.

Corporate Overview, Virage Incorporated web site; pp. 1-4.

Corripio, A. B., “Tuning of Industrial Control Systems”, InstrumentSociety of America, Research Triangle Park, N.C. (1990) pp. 65-81.

Cox, Ingemar J., et al., “PicHunter: Bayesian Relevance Feedback forImage Retrieval,” Proc. of the ICPR '96, IEEE, pp. 361-369.

Crawford et al., “Adaptive Pattern Recognition Applied To An ExpertSystem For Fault Diagnosis In Telecommunications Equipment”, pp. 10/1-8(Inspec. Abstract No. 86C010699, Inspec IEE (London) & IEE Coll. on“Adaptive Filters”, Digest No. 76, Oct. 10, 1985).

Cutting, D. R.; Karger, D. R.; Pedersen, J. O. & Tukey, J. W.“Scatter/Gather: A Cluster-based Approach to Browsing Large DocumentCollections”, 15 Ann. Int'l SIGIR '92, ACM, 1992, pp. 318-329.

Cvitanovic, Predrag: “Universality in Chaos”, Adam Hilger, Bristol,1989.

Daly, Donal: “Expert Systems Introduced”, Chartwell-Bratt, Lund, 1988.

Damashek, M., Gauging Similarity via N-Grams: Language-IndependentSorting, Categorization, and Retrieval of Text, pp. 1-11, Jan. 24, 1995.

Danielsson, Erik, et al.; “Computer Architectures for Pictorial Inf.Systems”; IEEE Computer, November, 1981; pp. 53-67.

Data Partner 1.0 Simplifies DB Query Routines, PC Week, Sep. 14, 1992,pp. 55 & 58.

Davis, Andrew W., “Hi Grandma!: Is It Time for TV Set POTSVideoconferencing?”, Advanced Imaging, pp. 45-49 (March 1997).

Davis, Andrew W., “The Video Answering Machine: Intel ProShare's NextStep”, Advanced Imaging, pp. 28-30 (March 1997).

Davis, Fred, “The Great Look-and-Feel Debate”, A+, 5:9-11 (July 1987).

Deering, S.; Estrin, D.; Farinacci, D.; Jacobson, V.; Liu, C.; Wei, L;“An Architecture for Wide-Area Multicast Routing”, ComputerCommunication Review, vol. 24, No. 4, October 1994, Proceedings ofSIGCOMM 94, pp. 126-135.

Dehning, Waltraud, Essig Heidrun, and Maass, Susanne, The Adaptation ofVirtual Man-Computer Interfaces to User Requirements in Dialogs,Germany: Springer-Verlag, 1981.

Dempster, A. P., “A generalization of Bayesian inference”, Journal ofthe Royal Statistical Society, Vol. 30, Series B (1968).

Dempster, A. P., “Upper and lower probabilities induced by a multivaluedmapping”, Annals of mathematical Statistics, no. 38 (1967).

Denker; 1984 International Test Conf, October 1984, Philadelphia, Pa.;pp. 558-563.

Derra, Skip, “Researchers Use Fractal Geometry, .”, Research andDevelopment Magazine, March 1988.

Diggle, Peter J: “Time Series: A Biostatistical Introduction”, ClarendonPress, Oxford, 1990.

DivX standard.

Donnelley, J. E., “WWW media distribution via Hopewise ReliabeMulticast,” Computer Networks and ISDN Systems, vol. 27, No. 6, pp.81-788 (April, 1995).

Donovan, J., “Intel/IBM's Audio-Video Kernel”, Byte, December, 1991, pp.177-202.

Drazin, P G: “Nonlinear System”, Cambridge University Press, Cambridge,1992.

Dubois, D., “Modeles mathematiques de l'imprecis et de l'incertain envue d'applications aux techniques d'aide a la decision”, DoctoralThesis, University of Grenoble (1983).

Dubois, D., N. Prade, “Combination of uncertainty with belief functions:a reexamination”, Proceedings 9th International Joint Conference onArtificial Intelligence, Los Angeles (1985).

Dubois, D., N. Prade, “Fuzzy sets and systems-Theory and applications”,Academic Press, New York (1980).

Dubois, D., N. Prade, “Theorie des possibilites: application a larepresentation des connaissances en informatique”, Masson, Paris (1985).

Dubois, D.; “Modeles mathematiques de l'imprecis et de l'incertain envue d'applications aux techniques d'aide a la decision”; DoctoralThesis, University of Grenoble (1983).

Duda, R. O., P. E. Hart, M. J. Nilsson, “Subjective Bayesian methods forrule-based inference systems”, Technical Note 124-ArtificialIntelligence Center-SRI International.

Dukach, Semyon, “SNPP: A Simple Network Payment Protocol”, MITLaboratory for Computer Science, Cambridge, Mass., 1993.

Dukach, Seymon; Prototype Implementation of the SNPP Protocol;allspic.lcs.mit.edu; 1992.

Dunning, B. B., “Self-Learning Data-Base For Automated FaultLocalization”, IEEE, 1979, pp. 155-157.

EDN, May 11, 1995, pp. 40-106.

Edwards, John R., “Q&A: Integrated Software with Macros and anIntelligent Assistant”, Byte Magazine, January 1986, vol. 1, Issue 1,pp. 120-122, critiques the Intelligent Assistant by SymantecCorporation.

Ehrenreich, S. L., “Computer Abbreviations—Evidence and Synthesis”,Human Factors, 27(2):143-155 (April 1985).

Ekeland, Ivar: “Mathematics and the Unexpected”, The University ofChicago Press, Chicago, 1988 Falconer, Kenneth: “Fractal Geometry”, JohnWiley & Sons, Chichester, 1990.

Electronic Engineering Times (EET), Oct. 28, 1991, p. 62.

Electronic Engineering Times, Oct. 28, 1991, p. 62, “IBM Points a NewWay”.

Elliott, “Watch-Grab-Arrange-See: Thinking with Motion Images viaStreams and Collages”, Ph.D. Thesis, MIT, February 1993.

Elofson, G. and Konsynski, B., “Delegation Technologies: EnvironmentalScanning with Intelligent Agents”, Journal of Management InformationSystems, Summer 1991, vol. 8, Issue 1, pp. 37-62.

Elton, J., “An Ergodic Theorem for Iterated Maps”, Journal of ErgodicTheory and Dynamical Systems, 7 (1987).

Even et al; “Electronic Wallet”, pp. 383-386;1983.

Faloutsos, C., et al, “Efficient and Effective Querying by ImageContent”, Journal of Intelligent Information Systems:IntegratingArtificial Intelligence and Database Technologies, vol. 3-4, No. 3, July1994, pp. 231-262.

Farrelle, Paul M. and Jain, Anil K., “Recursive Block Coding-A NewApproach to Transform Coding”, IEEE Transactions on Communications, Com.34(2) (February 1986).

Fassihi, Theresa & Bishop, Nancy, “Cable Guide Courting NationalAdvertisers,” Adweek, Aug. 8, 1988.

Fisher Y, “Fractal image compression “, Siggraph 92.

Fitzpatrick, J. M., J. J. Grefenstette, D. Van Gucht, “ImageRegistration by Genetic Search”, Conf. Proc., IEEE Southeastcon 1984,pp. 460-464.

Flickner, et al. “Query by Image and Video Content, the QBIC System”,IEEE Computer 28(9); 23-32, 1995.

Foley, J. D., Wallace, V. L., Chan, P., “The Human Factor of ComputerGraphics Interaction Techniques”, IEEE CG&A, November 1984, pp. 13-48.

Foltz, P. W., Dumais, S. T., “Personalized Information Delivery: AnAnalysis Of Information Filtering Methods”, Communications of the ACM,December 1992, vol. 35, No. 12, pp. 51-60.

Fractal Image Compression Michael F. Barnsley and Lyman P. Hurd ISBN0-86720-457-5, ca. 250 pp.

Fractal Image Compression: Theory and Application, Yuval Fisher (ed.),Springer Verlag, N.Y., 1995. ISBN number 0-387-94211-4.

Fractal Modelling of Biological Structures, School of Mathematics,Georgia Institute of Technology (date unknown).

Franklin, Gene F, J David Powell & Abbas Emami-Naeini: “Feedback Controlof Dynamic Systems”, Addison-Wesley Publishing Co. Reading, 1994.

Freeman, W. T., et al, “The Design and Use of Steerable Filters”, IEEETransactions on Pattern Analysis and Machine Intelligence, vol. 15, No.9, September 1991, pp. 891-906.

Friedman, M. B., “An Eye Gaze Controlled Keyboard”, Proceedings of the2nd International Conference on Rehabilitation Engineering, 1984,446-447.

Fu, Sequential Methods in Pattern Recognition and Machine Learning,Academic, NY, N.Y. 1968.

Fua, P. V., “Using probability density functions in the framework ofevidential reasoning Uncertainty in knowledge based systems”, B.Bouchon, R. R. Yager, Eds. Springer Verlag (1987).

Garretson, R., “IBM Adds Drawing Assistant Design Tool to GraphicsSeries”, PC Week, Aug. 13, 1985, vol. 2, Issue 32, p. 8.

Gautama, S., D'Haeyer, J., “Learning Relational Models of Shape: A Studyof the Hypergraph Formalism”.

Gautama, S., D'Haeyer, J. P. F., “Context Driven Matching in StructuralPattern Recognition”.

Gellert, W, H Kustner, M Hellwich & H Kastner: “The VNR ConciseEncyclopedia of Mathematics”, Van Nostrand Reinhols Co., New York, 1975.

Gelman, A. D., et al.: A Store-And-Forward Architecture ForVideo-On-Demand Service; ICC 91 Conf.; June 1991; pp. 842-846.

George E P Box & Gwilym M Jenkins: “Time Series Analysis: Forecastingand Control”, Holden Day, San Francisco, 1976.

Gessler, S. and Kotulla A., “PDAs as mobile WWW browsers,” ComputerNetworks and ISDN Systems, vol. 28, No. 1-2, pp. 53-59 (December 1995).

Gevers, T., et al, “Enigma: An Image Retrieval System”, IEEE 11th IAPRInternational Conference On Pattern Recognition, 1992, pp. 697-700.

Gevers, T., et al, “Indexing of Images by Pictorial Information”, VisualDatabase Systems, II, 1992 IFIP, pp. 93-101.

Gifford, D., “Notes on Community Information Systems”, MIT LCS TM-419,December 1989.

Gifford, David K.; “Cryptographic Sealing for Information Secrecy andAuthentication”; Stanford University and Xerox Palo Alto ResearchCenter; Communication of the ACM; vol. 25, No. 4; April, 1982.

Gifford, David K.; “Digital Active Advertising”; U.S. patent applicationSer. No. 08/168,519; filed Dec. 16, 1993.

Gilfoil, D., and Mauro, C. L., “Integrating Human Factors and Design:Matching Human Factors Methods up to Product Development”, C. L. MauroAssoc., Inc., 1-7.

Gleick, James, “Chaos—Making a New Science”, Heinemann, London, 1988.

Gligor, Virgil D. et al.; “Object Migration and Authentication”; IEEETransactions on Software Engineering; vol. SE-5, No. 6; November, 1979.

Glinert-Stevens, Susan, “Microsoft Publisher: Desktop Wizardry”, PCSources, Feb., 1992, vol. 3, Issue 2, p. 357.

Goble, C., et al, “The Manchester Multimedia Information System”,Proceedings of IEEE Conference, Eurographics Workshop, April, 1991, pp.244-268.

Gogoussis et al., Proc. SPIE Intl. Soc. Opt. Eng., November 1984,Cambridge, Mass., pp. 121-127.

Goldberg, Cheryl, “IBM Drawing Assistant: Graphics for the EGA”, PCMagazine, Dec. 24, 1985, vol. 4, Issue 26, p. 255.

Gong et al, “An Image Database System with Content Capturing and FastImage Indexing Abilities”, PROC of the International Conference onMultimedia Computing and Systems, pp. 121-130 May 19, 1994.

Gong et al. “An Image Database System with Content Capturing and FastImage Indexing Abilities” IEEE, 1994, pp. 121-130, May 1994.

Gonzalez et al., Digital Image Processing, Addison-Wesley, Reading,Mass., 1977.

Gonzalez, Rafael C., “Digital Image Processing”, Addison-Wesley,Reading, Mass. (1987).

Gonzalez, Rafael C., “Digital Image Processing”, Addison-Wesley,Reading, Mass. (1987).

Gould, John D., Boies, Stephen J., Meluson, Antonia, Rasammy, Marwan,and Vosburgh, Ann Marie, “Entry and Selection Methods For SpecifyingDates”. Human Factors, 32(2):199-214 (April 1989).

Graf S, “Barnsley's Scheme for the Fractal Encoding of Images”, JournalOf Complexity, V8, 72-78 (1992).

Great Presentations advertisement: Remote, Remote; 1987; p. 32H.

Green, Lee, “Thermo Tech: Here's a common sense guide to the newthinking thermostats”, Popular Mechanics, October 1985, 155-159.

Grosky, W., et al, “A Pictorial Index Mechanism for Model-basedMatching”, Data 7 Knowledge Engineering 8, 1992, pp. 309-327.

Grosky, W., et al, “Index-based Object Recognition in Pictorial DataManagement”, Computer Vision, 1990, pp. 416-436.

Grossberg, S., G. Carpenter, “A Massively Parallel Architecture for aSelf-Organizing Neural Pattern Recognition Machine,” Computer Vision,Graphics, and Image Processing (1987, 37, 54-115), pp. 252-315.

Grudin, Jonathan, “The Case Against User Interface Consistency”, MCCTechnical Report Number ACA-HI-002-89, January 1989.

Gudivada, V. N., and V. V. Raghavan. Design and evaluation of algorithmsfor image retrieval by spatial similarity. ACM Trans. on InformationSystems, 13(2), April 1995.

Gudivada, V., et al, “A Spatial Similarity Measure for Image DatabaseApplications”, Technical Report 91-1, Department of Computer Science,Jackson, Miss., 39217, 1990-1991.

Guenther, O., and A. Buchmann. Research issues in spatial databases. InACM SIGMOD Record, volume 19, December 1990.

Gullichsen E., E. Chang, “Pattern Classification by Neural Network: AnExperiment System for Icon Recognition,” ICNN Proceeding on NeuralNetworks, March 1987, pp. IV-725-32.

Gupta, Amarnath; Weymount, Terry & Jain, Ramesh, “Semantic Queries WithPictures: The VIMSYS Model”, Proceedings of the 17th InternationalConference on Very Large Data Bases, pp. 69-79, Barcelona, September,1991.

Hafner, J., H. S. Sawhney, W. Equitz, M. Flickner, and W. Niblack.Efficient color histogram indexing for quadratic form distancefunctions. IEEE Trans. Pattern Anal. Machine Intell., July 1995.

Haines, R. W., “HVAC Systems Design Handbook”, TAB Professional andReference Books, Blue Ridge Summit, Pa. (1988) pp. 170-177.

Harris, C. J., & S. A. Billings, “Self-Tuning and Adaptive Control:Theory and Applications”, Peter Peregrinus LTD (1981) pp. 20-33.

Harty et al., “Case Study: The VISA Transaction Processing System,”1988.

Haruki, K. et al., “Pattern Recognition of Handwritten Phonetic JapaneseAlphabet Characters”, International Joint Conference on Neural Networks,Washington, D.C., January 1990, pp. 11-515 to II-518.

Harvey, Michael G., and Rothe, James T., “VideoCassette Recorders: TheirImpact on Viewers and Advertisers”, Journal of Advertising, 25:19-29(December/January 1985).

Hasegawa, J., et al, “Intelligent Retrieval of Chest X-Ray ImageDatabase Using Sketches”, System And Computers In Japan, 1989, pp.29-42.

Hawkins, William J., “Super Remotes”, Popular Science, February 1989,76-77.

Hayashi, Y., et al., “Alphanumeric Character Recognition Using aConnectionist Model with the Pocket Algorithm”, Proceedings of theInternational Joint Conference on Neural Networks, Washington, D.C. Jun.18-22, 1989, vol. 2, pp. 606-613.

Hayes, H. I.; Solka, J. L.; Priebe, C. E.; “Parallel computation offractal dimension”, Proceedings of the SPIE—The International Societyfor Optical Engineering, 1962:219-30 (1993).

Hendrix, Gary G. and Walter, Brett A., “The Intelligent Assistant:Technical Considerations Involved in Designing Q&A's Natural-languageInterface”, Byte Magazine, December 1987, vol. 12, Issue 14, p. 251.

Henke, Lucy L., and Donohue, Thomas R., “Functional Displacement ofTraditional TV Viewing by VCR Owners”, Journal of Advertising Research,29:18-24 (April-May 1989).

Hinton et al., “Boltzmann Machines: Constraint Satisfaction Networksthat Learn”, Tech. Report CMU-CS-85-119, Carnegie-Mellon Univ, 5/84.

Hirata, et al. “Query by Visual Example, Content Based Image Retrieval”Advance in Database Technology-EDBT '92, Springer-Verlag, Berlin 1992,pp. 56-71

Hirata, K., et al, “Query by Visual Example Content Based ImageRetrieval”, Advances In Database Technology, March, 1992, pp. 57-71.

Hirzalla et al., “A Multimedia Query User Interface”, IEEE on CD-ROM,pp. 590-593, Sep. 5, 1995.

Hirzinger, G., Landzettel, K., “Sensory Feedback Structures for Robotswith Supervised Learning”, IEEE Conf. on Robotics and Automation, St.Louis, March 1985.

Hoare, F.; de Jager, G., “Neural networks for extracting features ofobjects in images as a pre-processing stage to pattern classification”,Proceedings of the 1992 South African Symposium on Communications andSignal Processing. COMSIG '92 (Cat. No. 92TH0482-0). Inggs, M. (Ed.), p.239-42 (1992).

Hoban, Phoebe, “Stacking the Decks”, New York, Feb. 16, 1987, 20:14.

Hoffberg, Linda I, “AN IMPROVED HUMAN FACTORED INTERFACE FORPROGRAMMABLE DEVICES: A CASE STUDY OF THE VCR” Master's Thesis, TuftsUniversity (Master of Sciences in Engineering Design, November, 1990).

Hoffberg, Linda I., “Designing a Programmable Interface for a VideoCassette Recorder (VCR) to Meet a User's Needs”, Interface 91 pp.346-351 (1991).

Hoffberg, Linda I., “Designing User Interface Guidelines For Time-ShiftProgramming of a Video Cassette Recorder (VCR)”, Proc. of the HumanFactors Soc. 35th Ann. Mtg. pp. 501-504 (1991).

Hoffman, D. L. et al., “A New Marketing Paradigm for ElectronicCommerce,” (Feb. 19, 1996), http://www2000.ogsm.vanderbilt.edunovak/new.marketing.paradigm.html.

Hollatz, S. A., “Digital image compression with two-dimensional affinefractal interpolation functions”, Department of Mathematics andStatistics, University of Minnesota-Duluth, Technical Report 91-2.

Hong Kong Enterprise advertisement: Two Innovative New Consumer ProductsFrom SVI; November 1988; p. 379.

Hongjiang, et al., Digital Libraries, “A Video Database System forDigital Libraries”, pp. 253-264, May 1994.

Hooge, Charles, “Fuzzy logic Extends Pattern Recognition Beyond NeuralNetworks”, Vision Systems Design, January 1998, pp. 32-37.

Hopfield et al., “Computing with Neural Circuits: A Model”, Science,233:625-633 (8 Aug. 1986).

Hopfield, “Neural Networks and Physical Systems with Emergent CollectiveComputational Abilities”, Proc. Natl. Acad. Sci. USA, 79:2554-2558(April 1982).

Hopfield, “Neurons with graded response have collective computationalproperties like those of two-state neurons”, Proc. Natl. Acad. Sci. USA,81:3088-3092 (May 1984).

Hopfield; “Neural Networks and Physical Systems with Emergent CollectiveComputational Abilities”; Proc. Natl. Acad. Sci. USA; 79:2554-2558(April 1982).

Horgan, H., “Medical Electronics”, IEEE Spectrum, January 1984, pp.90-93.

Hou et al., “Medical Image Retrieval by Spatial Features”, IEEE onCD-ROM, pp. 1364-1369, Oct. 18, 1992.

Howard, Bill, “Point and Shoot Devices”, PC Magazine, 6:95-97 (August1987).

Hsu et al., “Pattern Recognition Experiments in the Mandala/CosineDomain”, IEEE Transactions on Pattern Analysis and Machine Intelligence,vol. PAMI-5, No. 5, September 1983, pp. 512-520.

Hu et al., “Pattern Recognition by Moment Invariants”, Proc. IRE, vol.49, 1961, p. 1428.

Hunter, Jane, “The Application of Metadata Standards to Video Indexing”http://www.dtsc.edu.au/RDU/staff/jane-hunter/EuroDL/final.html (<Dec.24, 1998).

Hurtgen, B.; Buttgen, P., “Fractal approach to low rate video coding”,Proceedings of the SPIE—The International Society for OpticalEngineering, 2094(pt.1):120-31(1993).

Hutheesing, H., “Interactivity for the passive”, Forbes magazine Dec. 6,1993 (@ Forbes Inc. 1993) (2 pages).

IEEE Communications Magazine; vol. 32, No. 5, May 1994 New York, N.Y.,US, pp. 68-80, XP 000451097 Chang et al “An Open Systems Approach toVideo on Demand”.

IEEE-1394.

Iino et al., “An Object-Oriented Model for Spatio-TemporalSynchronization of Multimedia Information”, May, 1994.

Information describing BroadVision One-to-One Application System:“Overview,” p. 1; Further Resources on One-To-One Marketing, p. 1;BroadVision Unleashes the Power of the Internet with PersonalizedMarketing and Selling, pp. 1-3; Frequently Asked Questions, pp. 1-3;Products, p. 1; BroadVision One-To-One(.TM.), pp. 1-2; Dynamic CommandCenter, p. 1; Architecture that Scales, pp. 1-2; Technology, pp. 1;Creating a New Medium for Marketing and Selling BroadVision One-To-Oneand the World Wide Web a White Paper, pp. 1-15;http://www.broadvision.com (January-March, 1996).

Information Network Institute, Carnegie Mellon University, InternetBilling Server, Prototype Scope Document, Oct. 14, 1993.

Information Processing 71, North-Holland Publishing Company (1972) pp.1530-1533.

Ingemar J. Cox et al., “Target Testing and the Pic Hunter BayesianMultimedia Retrieval System,” Proc. of the 3d Forum on Research andTechnology Advances in Digital Libraries, ADL '96, IEEE, pp. 66-75.

Intel Corporation, iPower Technology, Marketing Brochure, date unknown.

Intuit Corp. Quicken User's Guide, “Paying Bills Electronically”, pp.171-192; undated.

Ioka, M., “A Method of Defining the Similarity of Images on the Basis ofColor Information”, Bulletin Of The National Museum Of Ethnology SpecialIssue, pp. 229-244, No. 17, November 1992.

Irven, Judith H., et al., “Multi-Media Information Services: ALaboratory Study”, IEEE Communications Magazine, vol. 26, No. 6, June,1988, pp. 24-44.

Ishizuka, M., “Inference methods based on extended Dempster and Shafer'stheory for problems with uncertainty/fuzziness”, New GenerationComputing, 1:159-168 (1983), Ohmsha, Ltd, and Springer Verlag.

Ishizuka, M., “Inference methods based on extended Dempster and Shafer'stheory for problems with uncertainty/fuzziness”, New GenerationComputing, 1:159-168 (1983), Ohmsha, Ltd., and Springer Verlag.

ISO/IEC JTC1/SC29/WG11 N1733, MPEG97, July 1997, “MPEG-7 Context andObjectives (v.4—Stockholm)”.

ISO/IEC JTC1/SC29/WG11 N1735, MPEG97, July 1997—Stockholm, “MPEG-7Applications Document”.

ISO/IEC JTC1/SC29/WG11 N1920, MPEG97, October 1997 “MPEG-7 Context andObjectives (v.5—Fribourg)”.

ISO/IEC JTC1/SC29/WG11 N2460, MPEG98, October 1998 “MPEG-7 Context andObjectives (v.10—Atlantic City)”.

ISO/IEC JTC1/SC29/WG11 N2461, MPEG98, October 1998—Atlantic City,“MPEG-7 Requirements”.

ISO/IEC JTC1/SC29/WG11 N2462, MPEG98, October 1998—Atlantic City,“MPEG-7 Applications”.

ISO/IEC JTC1/SC29/WG11 N2467, MPEG98, October 1998—Atlantic City,“MPEG-7 Content Set”.

Iyengar et al., “Codes Designs for Image Browsing”, 1994.

Jackel, L. D., H. P. Graf, J. S. Denker, D. Henderson and I. Guyon, “AnApplication of Neural Net Chips: Handwritten Digit Recognition,” ICNNProceeding, 1988, pp. II-107-15.

Jacobs, Charles E., Finkelstein, Adam, Salesin, David H., “FastMultiresolution Image Querying”, Department of Computer Science,University of Washington, Seattle Wash.

Jacobs, E. W., Y. Fisher and R. D. Boss. “Image Compression: A study ofthe Iterated Transform Method. “Signal Processing 29, (1992) 25-263.

Jacquin, A., “Image Coding Based on a Fractal Theory of IteratedContractive Image Transformations “p. 18, January 1992 (Vol 1 Issue 1)of IEEE Trans on Image Processing.

Jacquin, A., “A Fractal Theory of Iterated Markov Operators withApplications to Digital Image Coding “, PhD Thesis, Georgia Tech, 1989.

Jacquin, A., ‘Fractal image coding based on a theory of iteratedcontractive image transformations’, Proc. SPIE Visual Communications andImage Processing, 1990, pages 227-239.

Jacquin, A. E., ‘A novel fractal block-coding technique for digitalimages’, Proc. ICASSP 1990.

Jane Pauley Special, NBC TV News Transcript, Jul. 17, 1990, 10:00 PM.

Jean, J. S. N., et al., “Input Representation and Output VotingConsiderations for Handwritten Numeral Recognition withBackpropagation”, International Joint Conference on Neural Networks,Washington, D.C., January 1990, pp. 1-408 to 1-411.

Jeffrey, R. J., “The logic of decision”, The University of ChicagoPress, Ltd., London (1983)(2nd Ed.).

Jim Binkley & Leslie Young, Rama: An Architecture for InternetInformation Filtering, Journal of Intelligent Information Systems:Integrating Artificial Intelligence and Database Technologies, vol. 5,No. 2, September 1995, pp. 81-99.

Jones, R., “Digital's World-Wide Web server: A case study,” ComputerNetworks and ISDN Systems, vol. 27, No. 2, pp. 297-306 (November 1994).

JPL New Technology Report NPO-20213, Nasa Tech Brief Vol. 22, No. 4,Item #156 (April 1998).

Kato, T., “A Sketch Retrieval Method for Full Color Image Database-Queryby Visual Example”, IEEE, Publication No. 0-8186-2910-X/92, 1992, pp.530-533.

Kato, T., “Intelligent Visual Interaction with Image Database SystemsToward the Multimedia Personal Interface”, Journal Of InformationProcessing, vol. 14, No. 2, 1991, pp. 134-143.

Kato, T., et al, “A Cognitive Approach Interaction”, InternationalConference Of Multimedia Information Systems, January, 1991, pp.109-119.

Kato, T., et al, “Trademark: Multimedia Database with AbstractedRepresentation on Knowledge Base”, Proceedings Of The SecondInternational Symposium On Interoperable Information Systems, pp.245-252, November 1988.

Kato, T., et al, “Trademark: Multimedia Image Database System withIntelligent Human Interface”, System And Computers In Japan, 1990, pp.33-46.

Kaufmann, A., “Introduction a la theorie des sous-ensembles flous”, Vol.1, 2 et 3-Masson-Paris (1975).

Kaye, Brian H: “A Random Walk Through Fractal Dimensions”, VCHVerlagsgesellschaft, Weinheim, 1989.

Keeney, R. L., B. Raiffa, “Decisions with multiple objectives:Preferences and value tradeoffs”, John Wiley and Sons, New York (1976).

Kellman, P., “Time Integrating Optical Signal Processing”, Ph. D.Dissertation, Stanford University, 1979, pp. 51-55.

Kelly et al. “Efficiency Issues Related to Probability Density FunctionComparison”, SPIE vol. 2670, pp. 42-49 January 1996.

Kelly, P. M., et al. “Candid Comparison Algorithm for Navigating DigitalImage Databases”, Proceedings 7th International Working Conference onScientific and Statistical Database Management, pp. 252-258, 1994.

Kim, D. H.; Caulfield, H. J.; Jannson, T.; Kostrzewski, A.; Savant, G,“Optical fractal image processor for noise-embedded targets detection”,Proceedings of the SPIE—The International Society for OpticalEngineering, Vol: 2026 p. 144-9 (1993) (SPIE Conf: Photonics forProcessors, Neural Networks, and Memories 12-15 July 1993, San Diego,Calif., USA).

Kim, Y., “Chips Deliver Multimedia”, Byte, December 1991, pp. 163-173.

Knowlton, K., “Virtual Pushbuttons as a Means of Person-MachineInteraction”, Proc of Conf. Computer Graphics, Pattern Recognition andData Structure, Beverly Hills, Calif., May 1975, pp. 350-352.

Koch, H., “Ergonomische Betrachtung von Schreibtastaturen”, HumaneProduction, 1, pp. 12-15 (1985).

Kohonen, “Self-Organization & Memory”, Second Ed., 1988,Springer-Verlag, pp. 199-209.

Kolson, Ann, “Computer wimps drown in a raging sea of technology”, TheHartford Courant, May 24, 1989, B1.

Kortegaard, B. L., “PAC-MAN, a Precision Alignment Control System forMultiple Laser Beams Self-Adaptive Through the Use of Noise”, Los AlamosNational Laboratory, date unknown.

Kortegaard, B. L., “Superfine Laser Position Control Using StatisticallyEnhanced Resolution in Real Time”, Los Alamos National Laboratory,SPIE-Los Angeles Technical Symposium, Jan. 23-25, 1985.

Kraiss, K. F., “Alternative Input Devices For Human ComputerInteraction”, Forschunginstitut Für Anthropotecahnik, Werthhoven, F. R.Germany.

Kraiss, K. F., “Neuere Methoden der Interaktion an der SchnittstelleMensch-Maschine”, Z. F. Arbeitswissenschaft, 2, pp. 65-70, 1978.

Krajewski, M. et al, “Applicability of Smart Cards to Network UserAuthentication”, Computing Systems, vol. 7, No. 1, 1994.

Krajewski, M., “Concept for a Smart Card Kerberos”, 15th NationalComputer Security Conference, October 1992.

Krajewski, M., “Smart Card Augmentation of Kerberos, Privacy andSecurity Research Group Workshop on Network and Distributed SystemSecurity”, February 1993.

Kraus, T. W., T. J. Myron, “Self-Tuning PID Controller Uses PatternRecognition Approach”, Control Engineering, pp. 106-111, June 1984.

Kreifeldt, J. G., “A Methodology For Consumer Product Safety Analysis”,The 3rd National Symposium on Human Factors in Industrial Design inConsumer Products, August 1982, 175-184.

Kreifeldt, John, “Human Factors Approach to Medical Instrument Design”,Electro/82 Proceedings, 3/3/1-3/3/6.

Ksienski et al., “Low Frequency Approach to Target Identification”,Proc. of the IEEE, 63(12):1651-1660 (December 1975).

Kuo, C.-C. J. (ed), “Multimedia Storage and Archiving Systems”, SPIEProc. Vol. 2916 (11/18-11/22/96).

Kuocheng, Andy Poing, and Ellingstad, Vernon S., “Touch Tablet and TouchInput”, Interface '87, 327.

Kurokawa, M., “An Approach to Retrieving Images by Using their PictorialFeatures”, IBM Research, Japan, September 1989.

Kyburg, H. E., “Bayesian and non Bayesian evidential updating”,Artificial Intelligence 31:271-293 (1987).

Lampson, Butler; Abadi, Martin; Burrows, Michael; and Wobber, Edward;“Authentication in Distributed Systems: Theory and Practice”; ACMTransactions on Computer Systems; vol. 10, No. 4; November, 1992; pp.265-310.

Landis, Sean, “Content-Based Image Retrieval Systems for InteriorDesign”,http://www.tc.cornell.edu/Visualization/Education/cs718/fall1995/landis/index.html.

Langton C G (ed): Artificial Life; Proceedings of the firstinternational conference on Artificial life, Redwood City:Addison-Wessley (1989).

Lauwerier, Hans: “Fractals—Images of Chaos”, Penguin Books, London,1991.

LeCun, Y. et al., “Handwritten Digit Recognition: Applications of Neural.”, IEEE Comm. Magazine, November 1989, pp. 41-46.

LeCun, Y., “Connectionism in Perspective”, in R. Pfeifer, Z. Schreter,F. Fogelman, L. Steels (Eds.), 1989, “Generalization and Network DesignStrategies”, pp. 143-155.

Ledgard, Henry, Singer, Andrew, and Whiteside, John, Directions in HumanFactors for Interactive Systems, New York, Springer-Verlag, 1981.

Lee et al., “Video Indexing—An Approach based on Moving Object andTrack”, Proceedings of Storage and Retrieval for Image and VideoDatabases, pp. 25-36. February 1993.

Lee, Denis, et al., “Query by Image Content Using Multiple Objects andMultiple Features: User Interface Issues,” 1994 Int'l Conf. on ImageProcessing, IEEE, pp. 76-80.

Lee, E., “Similarity Retrieval Techniques”, Pictorial InformationSystems, Springer Verlag, 1980 pp. 128-176.

Lee, Eric, and MacGregor, James, “Minimizing User Search Time MenuRetrieval Systems”, Human Factors, 27(2):157-162 (April 1986).

Lee, S., et al, “2D C-string: A New Spatial Knowledge Representation forImage Database Systems”, Pattern Recognition, vol. 23, 1990, pp.1077-1087.

Lee, S., et al, “Similarity Retrieval of Iconic Image Database”, PatternRecognition, vol. 22, No. 6 1989, pp. 675-682.

Lee, S., et al, “Spatial Reasoning and Similarity Retrieval of ImagesUsing 2D C-string Knowledge Representation”, Pattern Recognition, 1992,pp. 305-318.

Lendaris, G. G., and Stanely, G. L., “Diffraction Pattern Sampling forAutomatic Target Recognition”, Proc. IEEE 58:198-205 (1979).

Leon, Carol Boyd, “Selling Through the VCR”, American Demographics,December 1987, 40-43.

Li, H. Y., Y. Qiao and D. Psaltis, Applied Optics (April, 1993).

Liepins, G. E., M. R. Hilliard, “Genetic Algorithms: Foundations &Applications”, Annals of Operations Research, 21:31-58 (1989).

Lin, H. K., et al., “Real-Time Screen-Aided Multiple-Image OpticalHolographic Matched-Filter Correlator”, Applied Optics, 21(18):3278-3286(Sep. 15, 1982).

Liou, “Overview of the px64 kbit/s Video Coding Standard”,Communications of the ACM, vol. 34, No. 4, April 1991, pp. 60-63.

Lippmann, R. P., “An Introduction to Computing with Neural Nets”, IEEEASSP Magazine, 4(2):4-22 (April 1987).

Liu, Y., “Extensions of fractal theory”, Proceedings of the SPIE—TheInternational Society for Optical Engineering, 1966:255-68(1993).

Liu, Y., “Pattern recognition using Hilbert space”, Proceedings of theSPIE—The International Society for Optical Engineering, 1825:63-77(1992).

Ljung, Lennart, & Torsten Soderstrom: “Theory and Practice of RecursiveIdentification”, The MIT Press, Cambridge, Mass., 1983.

Ljung, Lennart: “System Identification; Theory for the User”,Prentice-Hall Englewood Cliffs, N.J., 1987.

Lloyd, Sheldon G., & Gerald D Anderson: “Industrial Process Control”,Fisher Controls Co., Marshalltown, 1971.

Loeb, S., “Architecting Personalized Delivery of MultimediaInformation”, Communications of the ACM, December 1992, vol. 35, No. 12,pp. 39-50.

Long, John, “The Effect of Display Format on the Direct Entry ofNumerical Information by Pointing”, Human Factors, 26(1):3-17 (February1984).

Lu, C., “Computer Pointing Devices: Living With Mice”, High Technology,January 1984, pp. 61-65.

Lu, C., “Publish It Electronically”, Byte, September 1993, pp. 94-109.

Mackay et al., “Virtual Video Editing in Interactive MultimediaApplications”, 1989.

Mahalanobis, A., et al., “Minimum Average Correlation Energy Filters”,Applied Optics, 26(17):3633-40 (Sep. 1, 1987).

Makridakis, Spyros, & Steven Wheelwright: “The Handbook of Forecasting”,John Wiley, N.Y., 1982.

Mandelbrot, Benoit: “Fractal Geometry of Nature”, W H Freeman and Co.,New York, 1983 (orig ed 1977).

Mandelbrot, Benoit: “Fractals—Form, Chance and Dimensions”, W H Freemanand Co., San Francisco, 1977.

Manners, George, “Smart Screens; Development of Personal NavigationSystems for TV Viewers,” Video Magazine, December 1993.

Mannes, G., “Smart Screens”, Video Magazine, December 1993) (2 Pages).

Mantei, Marilyn M., and Teorey, Toby J., “Cost/Benefit Analysis forIncorporating Human Factors in the Software Lifecycle”, Association forComputing Machinery, 1988.

Maragos, P., “Tutorial Advances in Morphological Image Processing”Optical Engineering 26:7:623-632 (1987).

Mardia, K V, J T Kent & J M Bibby: “Multivariate Analysis”, AcademicPress, London, 1979.

Martin, G. L. et al., “Recognizing Hand-Printed Letters and Digits UsingBackpropagation Learning”, Technical Report of the MCC, Human InterfaceLaboratory, Austin, Tex., January 1990, pp. 1-9.

Martinez et al. “Imagenet: A Global Distribution Database for ColorImage Storage and Retrieval in Medical Imaging Systems” IEEE, 1992,710-719, May 1992.

Masahiro Morita & Yoichi Shinoda, Information Filtering Based on UserBehavior Analysis and Best Match Text Retrieval, Proceedings of theSeventeenth Annual International ACM-SIGIR Conference on Research andDevelopment in Information Retrieval, Dublin, Jul. 3-6, 1994, PagesTitle Page (272)-281.

Mazel, D. S. , “Fractal Modeling of Time-Series Data”, PhD Thesis,Georgia Tech, 1991. (One dimensional, not pictures).

McAulay, A. D., J. C. Oh, “Image Learning Classifier System UsingGenetic Algorithms”, IEEE Proc. of the National Aerospace & ElectronicsConference, 2:705-710 (1989).

McCauley, Joseph L.: “Chaos, Dymanics, and Fractals”, CambridgeUniversity Press, Cambridge, 1993.

McFadden, M., “The Web and the Cookie Monster,” Digital Age, (August1996).

Meads, Jon A., “Friendly or Frivolous”, Datamation, Apr. 1, 1988,98-100.

Medvinsy et al, “NetCash: A Design for Practical Electronic Currency onthe Internet”, Proc. 1st ACM Conf. on Comp. and Comm. Security, November1993.

Medvinsy et al., “Electronic Currency for the Internet”, ElectronicMarkets, pp. 30-31, September 1993.

Mehrotra, R., et al, “Shape Matching Utilizing Indexed HypothesesGeneration and Testing”, IEEE Transactions On Robotics, vol. 5, No. 1,February 1989, pp. 70-77.

Meyer, J. A., Roitblat, H. L., Wilson, W. (eds.): From Animals toAnimats. Proceedings of the Second International Conference onSimulation of Adaptive Behaviour. Cambridge, Mass.: MIT Press. (1991).

Middleton, G. V. ed., 1991, Nonlinear Dynamics, Chaos and Fractals, withApplications to Geological Systems. Geol. Assoc. Canada Short CourseNotes Vol. 9 (available from the GAC at Memorial University ofNewfoundland, St. John's NF A1B 3X5).

Miller et al., “News On-Demand for Multimedia Networks”, ACMInternational Conference on Multimedia, Anaheim, Calif., 1-6, Aug. 1993,pp. 383-392.

Miller, R. K., Neural Networks ((c) 1989: Fairmont Press, Lilburn, Ga.),pp. 2-12 and Chapter 4, “Implementation of Neural Networks”, pp. 4-1 to4-26.

Mills et al., “A Magnifier Tool for Video Data”, Proceedings of ACMComputer Human Interface (CHI), May 3-7, 1992, pp. 93-98.

Mills, “Media Composition for Casual Users”, 1992.

Minka, T., “An Image Database Browser that Learns from UserInteraction”, Masters Thesis, Massachusetts Institute of Technology;1996; also appears as MIT Media Laboratory Technical Report 365.

Minneman et al., “Where Were We: making and using near-synchronous,pre-narrative video”, Multimedia '93, pp. 1-11. December 1993.

Molley, P., “Implementing the Difference-Squared Error Algorithm UsingAn Acousto-Optic Processor”, SPIE, 1098:232-239, (1989).

Molley, P., et al., “A High Dynamic Range Acousto-Optic Image Correlatorfor Real-Time Pattern Recognition”, SPIE, 938:55-65 (1988).

Moloney, Daniel M.: Digital Compression in Todays AddressableEnviroment; 1993 NCTA Technical Papers; Jun. 6, 1993; pp. 308-316.

Monro D M and Dudbridge F, “Fractal block coding of images “,Electronics Letters 28(11):1053-1054 (1992).

Monro D. M. & Dudbridge F. ‘Fractal approximation of image blocks’, ProcICASSP 92, pp. III: 485-488.

Monro D. M. ‘A hybrid fractal transform’, Proc ICASSP 93, pp. V: 169-72.

Monro D. M., Wilson D., Nicholls J. A. ‘High speed image coding with theBath Fractal Transform’, IEEE International Symposium on MultimediaTechnologies Southampton, April 1993.

Moore, T. G. and Dartnall, “Human Factors of a Microelectronic Product:The Central Heating Timer/Programmer”, Applied Ergonomics, 1983,13(1):15-23.

Mori, “Towards the construction of a large-scale neural network”,Electronics Information Communications Association Bulletin PRU 88-59,pp. 87-94.

Nadoli, Gajanana and Biegel, John, “Intelligent Agents in the Simulationof Manufacturing Systems”, Proceedings of the SCS Multiconference on AIand Simulation, 1989.

Nagasaka et al., “Automatic Video Indexing and Full-Video Search forObject Appearances”, Proceedings of the IFIP TC2/WG2.6 Second WorkingConference on Visual Database Systems, North Holland, (Knuth et al.,eds.), Sep. 30-Oct. 3, 1991, pp. 113-127, January 1992.

Naik et al., “High Performance Speaker Verification .”, ICASSP 86,Tokyo, CH2243-4/86/0000-0881, IEEE 1986, pp. 881-884.

National Westminster Bank Group Brochure; pp. 1-29; undated.

Needham, Roger M. and Schroeder, Michael D.; “Using Encryption forAuthentication in Large Networks of Computers”; Communications of theACM; vol. 21, No. 12; December, 1978; pp. 993-999.

Needham, Roger M.; “Adding Capability Access to Conventional FileServers”; Xerox Palo Alto Research Center; Palo Alto, Calif.

Negahdaripour, S., et al “Challenges in Computer Vision: Future ResearchDirection”, IEEE Transactions On Systems, Man And Cybernetics, pp.189-199, 1992, at Conference on Computer Vision and Pattern Recognition.

Netravali, Arun N., and Haskell, Barry G., “Digital PicturesRepresentation and Compression”, Plenum Press, New York (1988).

Newman, B. C., “Proxy-Based Authorization and Accounting for DistributedSystems”, Proc. 13th Int. Conf. on Dist. Comp. Sys., May 1993.

NewMedia, November/December 1991, p. 69.

Ney, H., et al., “A Data Driven Organization of the Dynamic ProgrammingBeam Search for Continuous Speech Recognition”, Proc. ICASSP 87, pp.833-836, 1987.

Niblack, W. et al., “The QBIC Project: Querying Images by Content UsingColor, Texture, and Shape”, IBM Computer Science Research Report, pp.1-20 (Feb. 1, 1993).

Niblack, W., et al, “Find me the Pictures that Look Like This: IBM'SImage Query Project”, Advanced Imaging, April 1993, pp. 32-35.

Niblack, W., R. Barber, W. Equitz, M. Flickner, E. Glasman, D. Petkovic,P. Yanker, and C. Faloutsos. The QBIC project: Querying images bycontent using color, texture, and shape. In Storage and Retrieval forImage and Video Databases, volume SPIE Vol. 1908, February 1993.

Nilsson, B. A., “Microsoft Publisher is an Honorable Start for DTPBeginners”, Computer Shopper, February 1992, vol. 12, Issue 2, p. 426,evaluates Microsoft Publisher and Page Wizard.

Nilsson, N. J., The Mathematical Foundations of Learning Machines ((c)1990: Morgan Kaufmann Publishers, San Mateo, Calif.) and particularlysection 2.6 “The Threshold Logic Unit (TLU)”, pp. 21-23 and Chapter 6,“Layered Machines” pp. 95-114.

Norman, D. A., Fisher, D., “Why Alphabetic Keyboards Are Not Easy ToUse: Keyboard Layout Doesn't Much Matter”, Human Factors 24(5), pp.509-519 (1982).

Norman, Donald A., “Infuriating By Design”, Psychology Today,22(3):52-56 (March 1988).

Norman, Donald A., The Psychology of Everyday Things, New York, BasicBook, Inc. 1988.

Novak et al., “Anatomy of a Color Histogram”, Proceeding of ComputerVision and Pattern Recognition, Champaign, Ill., June 1992, pp. 599-605.

Nussbaumer et al., “Multimedia Delivery on Demand: Capacity Analysis andImplications”, Proc 19th Conference on Local Computer Networks, 2-5 Oct.1994, pp. 380-386.

O'Connor, Rory J., “Apple Banking on Newton's Brain”, San Jose MercuryNews, Wednesday, Apr. 22, 1992.

O'Docherty, M. H., et al, “Multimedia Information System—The Managementand Semantic Retrieval of all Electronic Data Types”, The ComputerJournal, vol. 34, No. 3, 1991.

Ohsawa, I. and Yonezawa, A., “A Computational Model of an IntelligentAgent Who Talks with a Person”, Research Reports on InformationSciences, Series C, April 1989, No. 92, pp. 1-18.

Ohsuga et al, “Entrainment of Two Coupled van der Pol Oscillators by anExternal Oscillation”, Biological Cybernetics, 51:225-239 (1985).

Oien, G. E., S. Lepsoy & T. A. Ramstad, ‘An inner product space approachto image coding by contractive transformations’, Proc. ICASSP 1991, pp2773-2776.

Okada, Y., et al., “An Image Storage and Retrieval System for TextilePattern Adaptable to Color Sensation of the Individual”, Trans. Inst.Elec. Inf. Comm., vol. J70D, No. 12, pp. 2563-2574, December 1987(Japanese w/English Abstract).

Okamoto et al; “Universal Electronic Cash”, pp. 324-337; 1991.

Omata et al, “Holonic Model of Motion Perception”, IEICE TechnicalReports, Mar. 26, 1988, pp. 339-346.

O'Neal et al., “Coding Isotropic Images”, November 1977, pp. 697-707.

Ono, Atsushi, et al., “A Flexible Content-Based Image Retrieval Systemwith Combined Scene Description Keyword,” Proc. of Multimedia '96, IEEE,pp. 201-208.

Optical Engineering 28:5 (May 1988)(Special Issue on productinspection).

Page, G F, J B Gomm & D Williams: “Application of Neural Networks toModelling and Control”, Chapman & Hall, London, 1993.

Pandit, S. M., & S. M. Wu, “Timer Series & System Analysis withApplications”, John Wiley & Sons, Inc., NY (1983) pp. 200-205.

Pawlicki, T. F., D. S. Lee, J. J. Hull and S. N. Srihari, “NeuralNetwork Models and their Application to Handwritten Digit Recognition,”ICNN Proceeding, 1988, pp. II-63-70.

Pazzani, M. et al., “Learning from hotlists and coldlists: Towards a WWWInformation Filtering and Seeking Agent,” Proceedings InternationalConference on Tools with Artificial Intelligence, January 1995, pp.492-495.

Pecar, Branko: “Business Forecasting for Management”, McGraw-Hill BookCo., London, 1994.

Peitgen, Heinz-Otto, & Deitmar Saupe: “The Science of Fractal Images”,Springer-Verlag, N.Y., 1988.

Peitgen, Heinz-Otto, Hartmut Jurgens & Deitmar Saupe: “Fractals for theClassroom”, Springer-Verlag, 1992.

Perry et al., “Auto-Indexing Storage Device”, IBM Tech. Disc. Bulletin,12(8):1219 (January 1970).

Perspectives: High Technology 2, 1985.

Peters: “Chaos and Order in the Capital Markets”, Wiley, 1991.Gershenfeld & Weigend: “The Future of Time Series”, Addison-Wesley,1993.

Peterson, Ivars, “Packing It In-Fractals .”, Science News,131(18):283-285 (May 2, 1987).

Peterson, Ivars: “The Mathematical Tourist”, W H Freeman, New York,1988.

Petrakis, E. G. M., and C. Faloutsos. Similarity searching in largeimage databases. Technical Report 3388, Department of Computer Science,University of Maryland, 1995.

Pettit, Frank: “Fourier Transforms in Action”, Chartwell-Bratt, Lund,1985.

Pfitzmann et al; “How to Break and Repair a Provably Secure UntraceablePayment System”; pp. 338-350; 1991.

Phillips, “MediaView: a general multimedia digital publication system”,Comm. of the ACM, v. 34, n. 7, pp. 75-83. July 1991.

Picard et al. “Finding Similar Patterns in Large Image Databases”, IEEE,1993, pp. 161-164, April 1993.

Picard, R. W., et al, “finding Similar Patterns in Large ImageDatabases”, IEEE ICASSP, Minneapolis, Minn., vol. V, pp. 161-164, April1993; also appears in MIT Media Laboratory Technical Report No. 205.

Pickover, Cliff, Visions of the Future: Art, Technology, and Computingin the 21st Century (St. Martin's Press).

Pickover, Cliff, Chaos in Wonderland: Visual Adventures in a FractalWorld (St. Martin's Press).

Pickover, Cliff, Computers and the Imagination (St. Martin's Press).

Pickover, Cliff, Computers, Pattern, Chaos, and Beauty (St. Martin'sPress).

Pickover, Cliff, Frontiers of Scientific Visualization (Wiley).

Pickover, Cliff, Mazes for the Mind: Computers and the Unexpected (St.Martin's Press).

Pickover, Cliff, Spiral Symmetry (World Scientific).

Pizano, A., et al, “Communicating with Pictorial Databases”,Human-Machine Interactive Systems, pp. 61-87, Computer Science Dept,UCLA, 1991.

Platte, Hans-Joachim, Oberjatzas, Gunter, and Voessing, Walter, “A NewIntelligent Remote Control Unit for Consumer Electronic Device”, IEEETransactions on Consumer Electronics, Vol. CE-31(1):59-68 (February1985).

Poor, Alfred, “Microsoft Publisher”, PC Magazine, Nov. 26, 1991, vol.10, Issue 20, p. 40, evaluates Microsoft Publisher.

Port, Otis, “Wonder Chips-How They'll Make Computing Power Ultrafast andUltracheap”, Business Week, Jul. 4, 1994, pp. 86-92.

Press, William H. et al, “Numerical Recipes in C The Art of ScientificComputing”, Cambridge University Press, 1988.

Price, R., et al., “Applying Relevance Feedback to a Photo ArchivalSystem”, Journal of Information Science 18, pp. 203-215 (1992).

Priebe, C. E.; Solka, J. L.; Rogers, G. W., “Discriminant analysis inaerial images using fractal based features”, Proceedings of the SPIE—TheInternational Society for Optical Engineering, 1962:196-208(1993).

PRNewswire, information concerning the PointCast Network (PCN) (February13, 1996) p. 213.

Proakis, John G., Digital Communications, McGraw-Hill (1983).

Proceedings of the IEEE, vol. 82, No. 4, April 1994 New York, N.Y., US,pp. 585-589, XP 000451419 Miller “A Scenario for the Deployment ofInteractive Multimedia Cable Television Systems in the United States inthe 1990's”.

Proceedings, 6th International Conference on Pattern Recognition 1982,pp. 152-136.

Psaltis, D., “Incoherent Electro-Optic Image Correlator”, OpticalEngineering, 23(1):12-15 (January/February 1984).

Psaltis, D., “Two-Dimensional Optical Processing Using One-DimensionalInput Devices”, Proceedings of the IEEE, 72(7):962-974 (July 1984).

Quinell, Richard A., “Web Servers in embedded systems enhance userinteraction”, EDN, Apr. 10, 1997, pp. 61-68.

Raggett, D., “A review of the HTML+document format,” Computer Networksand ISDN Systems, vol. 27, No. 2, pp. 35-145 (November 1994).

Rahmati, M.; Hassebrook, L. G., “Intensity- and distortion-invariantpattern recognition with complex linear morphology”, PatternRecognition, 27 (4):549-68(1994).

Rampe, Dan, et al. In a Jan. 9, 1989 news release, Claris Corporationannounced two products, SmartForm Designer and SmartForm Assistant,which provide “Intelligent Assistance”, such as custom help messages,choice lists, and data-entry validation and formatting.

Rangan et al., “A Window-based Editor for Digital Video and Audio”,January 1992.

Rao et al., Discrete Cosine Transform—Algorithms Advantages,Applications, Academic Press, Inc., 1990.

Ratcliffe, Mitch and Gore, Andrew, “Intelligent Agents take U.S. Bows.”,MacWeek, Mar. 2, 1992, vol. 6, No. 9, p. 1.

Ravichandran, G. and Casasent, D., “Noise and Discrimination Performanceof the MINACE Optical Correlation Filter”, Proc. SPIE TechnicalSymposium, April 1990, Orlando Fla., 1471 (1990).

Reimer, “Memories in my Pocket”, Byte, pp. 251-258, February 1991.

Reiss, “The Revised Fundamental Theorem of Moment Invariants”, IEEETransactions on Pattern Analysis and Machine Intelligence, vol. 13, No.8, August 1991, pp. 830-834.

Reitman, Edward: “Exploring the Geometry of Nature”, Windcrest Books,Blue Ridge Summit, 1989.

Reusens, E., “Sequence coding based on the fractal theory of iteratedtransformations systems”, Proceedings of the SPIE—The InternationalSociety for Optical Engineering, 2094(pt.1):132-40(1993).

Rhodes, W., “Acousto-Optic Signal Processing: Convolution andCorrelation”, Proc. of the IEEE, 69(1):65-79 (January 1981).

Richards et al., “The Interactive Island”, IEE Revies, July/August 1991pp. 259-263.

Richards J., and Casasent, D., “Real Time Hough Transform for IndustrialInspection” Proc. SPIE Technical Symposium, Boston 1989 1192:2-21(1989).

Rivest, R.; “The MD5 Message-Digest Algorithm”; MIT Laboratory forComputer Science and RSA Data Security, Inc.; April, 1992.

Rivest, R. L. et al., “A Method for Obtaining Digital Signatures andPublic-Key Cryptosystems,” Laboratory for Computer Science,Massachusetts Institute of Technology, Cambridge, Mass.

Rivest, R. L.; Shamir, A. & Adleman, L.; “A Method for Obtaining DigitalSignatures and Public-Key Cryptosystems”, Communications of the ACM,February 1978, vol. 21, No. 2, pp. 120-126.

Robinson, G., and Loveless, W., “Touch-Tone' Teletext—A CombinedTeletext-Viewdata System,” IEEE Transactions on Consumer Electronics,vol. CE-25, No. 3, July 1979, pp. 298-303.

Rogus, John G. and Armstrong, Richard, “Use of Human EngineeringStandards in Design”, Human Factors, 19(1):15-23 (February 1977).

Rohrer, C., & Clay Nesler, “Self-Tuning Using a Pattern RecognitionApproach”, Johnson Controls, Inc., Research Brief 228 (Jun. 13, 1986).

Roizen, Joseph, “Teletext in the USA,” SMPTE Journal, July 1981, pp.602-610.

Rosch, Winn L., “Voice Recognition: Understanding the Master's Voice”,PC Magazine, Oct. 27, 1987, 261-308.

Rose, D. E.; Mander, R.; Oren, T., Ponceleon, D. B.; Salomon, G. & Wong,Y. Y. “Content Awareness in a File System Interface Implementing thePile Metaphor for Organizing Information”, 16 Ann. Int'l SIGIR '93, ACM,pp. 260-269.

Rosenfeld, Azriel and Avinash C. Kak, Digital Picture Processing, SecondEdition, Volume 2, Academic Press, 1982.

Roy, B., “Classements et choix en presence de points de vue multiples”,R.I.R.O.-2eme annee-no. 8, pp. 57-75 (1968).

Roy, B., “Electre III: un algorithme de classements fonde sur unerepresentation floue des preferences en presence de criteres multiples”,Cahiers du CERO, 20(1):3-24 (1978).

Rui, Yong, Huang, Thomas S., Chang, Shih-Fu, “Image Retrieval: PastPresent and Future”.

Rui, Yong, Huang, Thomas S., Mehotra, Sharad, “Browsing and retrievingVideo Content in a Unified Framework”.

Rui, Yong, Huang, Thomas S., Ortega, Michael, Mehotra, Sharad,“Relevance Feedback: A Power Tool for Interactive Content-Based ImageRetrieval”.

Rumelhart, D. E., & James L McClelland, Parallel Distributed Processing,Explorations in Microstructure of Cognition, vol. 1, (1986: MIT Press,Cambridge, Mass.), and specifically Chapter 8 thereof, “LearningInternal Representations by Error Propagation”, pp. 318-362.

Rutherford, H. G., F. Taub and B. Williams, “Object Identification andMeasurement from Images with Access to the Database to Select SpecificSubpopulations of Special Interest”, May 1986.

Rutter et al., “The Timed Lattice-A New Approach To Fast ConvergingEqualizer Design”, pp. VIII/1-5 (Inspec. Abstract No. 84C044315, InspecIEE (London) & IEE Saraga Colloquium on Electronic Filters, May 21,1984).

Sadjadi, F., “Experiments in the use of fractal in computer patternrecognition”, Proceedings of the SPIE—The International Society forOptical Engineering, 1960:214-22(1993).

Sakoe, H., “A Generalization of Dynamic Programming Based PatternMatching Algorithm Stack DP-Matching”, Transactions of the Committee onSpeech Research, The Acoustic Society of Japan, p. S83-23, 1983.

Sakoe, H., “A Generalized Two-Level DP-Matching Algorithm for ContinuousSpeech Recognition”, Transactions of the IECE of Japan, E65(11):649-656(November 1982).

Salomon et al, “Using Guides to Explore Multimedia Databases”, PROC ofthe Twenty-Second Annual Hawaii International Conference on SystemSciences. vol. IV, 3-6 Jan. 1989, pp. 3-12 vol. 4. Jan. 6, 1989.

Salton, G., “Developments in Automatic Text Retrieval”, Science, vol.253, pp. 974-980, Aug. 30, 1991.

Samet, H., The quadtree and related hierarchical data structures. ACMComputing Surveys, 16(2):187-260, 1984.

Sarver, Carleton, “A Perfect Friendship”, High Fidelity, 39:42-49 (May1989).

Schamuller-Bichl, I., “IC-Cards in High-Security Applications”, inSelected Papers from the Smart Card 2000 Conference, Springer Verlag,1991, pp. 177-199.

Scharlic, A., “Decider sur plusieurs criteres. Panorama de l'aide a ladecision multicritere” Presses Polytechniques Romandes (1985).

Schied, Francis, “Shaum's Outline Series-Theory & Problems of NumericalAnalysis”, McGraw-Hill Book Co., NY (1968) pp. 236, 237, 243, 244, 261.

Schmitt, Lee, “Let's Discuss Programmable Controllers”, Modern MachineShop, May 1987, 90-99.

Schniederman, Ben, Designing the User Interface: Strategies forEffective Human-Computer Interaction, Reading, Mass., Addison-Wesley,1987.

Schroeder, M., Fractals, Chaos, Power Laws, W.H. Freeman & Co., New York(1991).

Schurmann, J., “Zur Zeichen und Worterkennung beim AutomatischenAnschriftenlesen”, Wissenschaftlichl, Berichte, 52(1/2) (1979).

Scientific American; “Not Just a Pretty Face”; March 1990, pp. 77-78.

Seborg, D. E., T. F. Edgar, & D. A. Mellichamp, “Process Dynamics andControl”, John Wiley & Sons, NY (1989) pp. 294-307, 538-541.

Shafer, G., “A mathematical theory of evidence”, Princeton UniversityPress, Princeton, N.J. (1976).

Shann et al. “Detection of Circular Arcs for Content-Based Retrievalfrom an Image Database” IEE Proc.-Vis. Image Signal Process, vol. 141,No. 1, February 1994, pp. 49-55.

Shardanand, Upendra, “Social Information Filtering for MusicRecommendation” September 1994, pp. 1-93, Massachusetts Institute ofTechnology, Thesis.

Sharif Heger, A. and Koen, B. V., “KNOWBOT: an Adaptive Data BaseInterface”, Nuclear Science and Engineering, February 1991, vol. 107,No. 2, pp. 142-157.

Sharpless, “Subscription teletext for value added services”, August1985.

Shepard, J. D., “Tapping the Potential of Data Compression”, Militaryand Aerospace Electronics, May 17, 1993, pp. 25-27.

Sheth et al., “Evolving Agents for Personalized Information Filtering”,1-5 Mar. 1993, pp. 345-352.

Sheth, B. & Maes, P. “Evolving Agents For Personalized InformationFiltering”, Proc. 9th IEEE Conference, 1993 pp. 345-352.

Shimizu et al, “Principle of Holonic Computer and Holovision”, Journalof the Institute of Electronics, Information and Communication,70(9):921-930 (1987).

Shinan et al., “The Effects of Voice Disguise .”, ICASSP 86, Tokyo,CH2243-4/86/0000-0885, IEEE 1986, pp. 885-888.

Silverston et al., “Spectral Feature Classification and Spatial PatternRec.”, SPIE 201:17-26, Optical Pattern Recognition (1979).

Simpson, W. R., C. S. Dowling, “WRAPLE: The Weighted Repair AssistanceProgram Learning Extension”, IEEE Design & Test, 2:66-73 (April 1986).

Sincoskie, W. D. & Cotton C. J. “Extended Bridge Algorithms for LargeNetworks”, IEEE Network, January 1988-vol. 2, No. 1, pp. 16-24.

Sirbu, Marvin A.; Internet Billing Service Design And PrototypeImplementation; pp. 1-19; An Internet Billing Server.

Smith et al., “A New Family of Algorithms for Manipulating CompressedImages”, IEEE Computer Graphics and Applications, 1993.

Smith, J. et al., “Quad-Tree Segmentation for Texture-Based Image Query”Proceeding ACM Multimedia 94, pp. 1-15, San Francisco, 1994.

Smith, J. R., and S.-F. Chang. Querying by color regions using theVisualSEEk content-based visual query system. In M. T. Maybury, editor,Intelligent Multimedia Information Retrieval. IJCAI, 1996.

Smith, J. R., and S.-F. Chang. Tools and techniques for color imageretrieval. In Symposium on Electronic Imaging: Science andTechnology—Storage & Retrieval for Image and Video Databases IV, volume2670, San Jose, Calif., February 1996. IS&T/SPIE.

Smith, Sidney J., and Mosier, Jane N., Guidelines for Designing UserInterface Software, Bedford, Mass., MITRE, 1986.

Smoliar, S. et al., “Content-Based Video Indexing and Retrieval”, IEEEMultimedia, pp. 62-72 (Summer 1994).

Society for Worldwide Interbank Financial Telecommunications S.C.,“A.S.W.I.F.T. Overview”, undated.

Soffer, A., and H. Samet. Retrieveal by content in symbolic-imagedatabases. In Symposium on Electronic Imaging: Science andTechnology—Storage & Retrieval for Image and Video Databases IV, pages144-155. IS&T/SPIE, 1996.

Soviero, Marcelle M., “Your World According to Newton”, Popular Science,September 1992, pp. 45-49.

Specht, IEEE Internatl. Conf. Neural Networks, 1:1525-1532 (July 1988),San Diego, Calif. Sperling, Barbara Bied, Tullis Thomas S., “Are You aBetter ‘Mouser’ or ‘Trackballer’? A Comparison of Cursor—PositioningPerformance”, An Interactive/Poster Session at the CHI+GI'87 GraphicsInterface and Human Factors in Computing Systems Conference.

Sprageu, R. A., “A Review of Acousto-Optic Signal Correlators”, OpticalEngineering, 16(5):467-74 (September/October 1977).

Sprinzak, J.; Werman, M., “Affine point matching”, Pattern RecognitionLetters, 15(4):337-9(1994).

Stanchev, P., et al, “An Approach to Image Indexing of Documents”,Visual Database Systems, II, 1992, pp. 63-77.

Stanley R. Sternberg, “Biomedical Image Processing”, IEEE Computer,1983, pp. 22-34.

Stark, J., “Iterated function systems as neural networks “, NeuralNetworks, Vol 4, pp 679-690, Pergamon Press, 1991.

Stevens, “Next Generation Network and Operating System Requirements forContinuous Time Media”, in Herrtwich (Ed.), Network and Operating SystemSupport for Digital Audio and Video, pp. 197-208, November 1991.

Stewart, R. M., “Expert Systems For Mechanical Fault Diagnosis”, IEEE,1985, pp. 295-300.

Streeter, L. A., Ackroff, J. M., and Taylor, G. A. “On AbbreviatingCommand Names”, The Bell System Technical Journal, 62(6):1807-1826(July/August 1983).

Stricker, M., and A. Dimai. Color indexing with weak spatialconstraints. In Symposium on Electronic Imaging: Science andTechnology—Storage & Retrieval for Image and Video Databases IV, pages29-41. IS&T/SPIE, 1996.

Stricker, M., and M. Orengo. Similarity of color images. In Storage andRetrieval for Image and Video Databases III, volume SPIE Vol. 2420,February 1995.

Sugeno, M., “Theory of fuzzy integrals and its applications”, TokyoInstitute of Technology (1974).

Svetkoff et al.; Hybrid Circuits (GB), No. 13, May 1987; pp. 5-8.

Swain et al., “Color Indexing”, International Journal of ComputerVision, vol. 7, No. 1, 1991, pp. 11-32.

Swanson, David, and Klopfenstein, Bruce, “How to Forecast VCRPenetration”, American Demographic, December 1987, 44-45.

Tak W. Yan & Hector Garcia-Molina, SIFT—A Tool for Wide-Area InformationDissemination, 1995 USENIX Technical Conference, New Orleans, La.,January 16-20, pp. 177-186.

Tamura, H., et al, “Image Database Systems: A Survey”, PatternRecognition, vol. 17, No. 1, 1984, pp. 29-34.

Tamura, H., et al., “Textural Features Corresponding to VisualPerception, “IEEE Transactions on System, Man, and Cyb., vol. SMC-8, No.6, pp. 460-473 (1978).

Tanaka, S., et al, “Retrieval Method for an Image Database based onTopological Structure”, SPIE, vol. 1153, 1989, pp. 318-327.

Tanton, N. E., “UK Teletext—Evolution and Potential,” IEEE Transactionson Consumer Electronics, vol. CE-25, No. 3, July 1979, pp. 246-250.

TCC Tech Facts, Vols. 1-4, (www.wgbh.org, rev. September 1995).

Television Decoder Circuitry Act of 1990, and Section 305 of theTelecommunications Act of 1996, and FCC regulations.

Tello, Ernest R., “Between Man And Machine”, Byte, September 1988,288-293.

Tenenbaum, Jay M. and Schiffman, Allan M.; “Development of NetworkInfrastructure and Services for Rapid Acquisition”; adapted from a whitepaper submitted to DARPA by MCC in collaboration with EIT and ISI.

Thomas, John, C., and Schneider, Michael L., Human Factors in ComputerSystems, New Jersey, Ablex Publ. Co., 1984.

Thomas, William L., “Electronic Program Guide Applications—The Basics ofSystem Design”, 1994 NCTA Technical Papers, pp. 15-20.

Tonomura et al., “Content Oriented Visual Interface Using Video Iconsfor Visual Database Systems”, Journal of Visual Languages and Computing(1990) 1, pp. 183-198.

Tonomura et al., “VideoMAP and VideoSpacelcon: Tools for AnatomizingVideo Content”, Inter CHI'93 Conference Proceedings, Amsterdam, TheNetherlands, 24-29 Apr., 1993, pp. 131-136.

Tortora, G., et al, “Pyramidal Algorithms”, Computer Vision, Graphicsand Images Processing, 1990, pp. 26-56.

Trachtenberg, Jeffrey A., “How do we confuse thee? Let us count theways”, Forbes, Mar. 21, 1988, 159-160.

Training Computers To Note Images, New York Times, Apr. 15, 1992.

Turcotte, Donald L., 1992, Fractals and Chaos in Geology and Geophysics.Cambridge U.P.

TV Communications Advertisement for MSI Datacasting Systems, January1973.

Tyldesley, D. A., “Employing Usability Engineering in the Development ofOffice Products”, The Computer Journal”, 31(5):431-436 (1988).

Udagawa, K., et al, “A Parallel Two-Stage Decision Method forStatistical Character Recognition .”, Electronics and Communications inJapan (1965).

Ueda et al., “Automatic Structure Visualization for Video Editing”,InterCHI'93 Conference Proceedings, Amsterdam, The Netherlands, 24-29Apr. 1993, pp. 137-141.

Ueda et al., “Impact: An Interactive Natural-Motion-Picture DedicatedMultimedia Authoring System”, Proceedings of Human Factors in ComputingSystems (CHI 91), New Orleans, La., Apr. 27-May 2, 1991, pp. 343-350.

van den Boom, Henrie: An Interactive Videotex System for Two-Way CATVNetworks; AEU, Band 40; 1986; pp. 397-401.

Vander Lugt, A., “Practical Considerations for the Use of SpatialCarrier-Frequency Filters”, Applied Optics, 5(11):1760-1765 (November1966).

Vander Lugt, A., “Signal Detection By Complex Spatial Filtering”, IEEETransactions On Information Theory, IT-10, 2:139-145 (April 1964).

Vander Lugt, A., et al.; “The Use of Film Nonlinearites in OpticalSpatial Filtering”; Applied Optics; 9(1):215-222 (January 1970).

Vannicola et al, “Applications of Knowledge based Systems toSurveillance”, Proceedings of the 1988 IEEE National Radar Conference,20-21 Apr. 1988, pp. 157-164.

Varela, F. J., and P. Bourgine (eds.): Proceedings of the first EuropeanConference on Artificial Life. Cambridge, Mass.: MIT Press. (1991).

Verplank, William L., “Graphics in Human-Computer Communication:Principles of Graphical User-Interface Design”, Xerox Office Systems.

Vitols, “Hologram Memory for Storing Digital Data”, IBM Tech. Disc.Bulletin 8(11):1581-1583 (April 1966).

Vittal, J., “Active Message Processing: Message as Messengers”, pp.175-195; 1981.

Voydock, Victor et al.; “Security Mechanisms in High-Level NetworkProtocols”; Computing Surveys; vol. 15, No. 2; June 1981.

Voyt, Carlton F., “PLC's Learn New Languages”, Design News, Jan. 2,1989, 78.

Vrscay, Edward R. “Iterated Function Systems: Theory, Applications, andthe Inverse Problem. “Fractal Geometry and Analysis, J. Belair and S.Dubuc (eds.) Kluwer Academic, 1991. 405-468.

Wachman, J., “A Video Browser that Learns by Example”, Masters Thesis,Massachusetts Institute of Technology; 1996; also appears as MIT MediaLaboratory Technical Report No. 383.

Wakimoto, K., et al, “An Intelligent User Interface to an Image Databaseusing a Figure interpretation Method”, IEEE Publication No.CH2898-5/90/0000/0516, 1990, pp. 516-520.

Wald; Sequential Analysis; Dover Publications Inc., 1947; pp. 34-43.

Wallace, “The JPEG Still Picture Compression Standard”, Communicationsof the ACM, vol. 34, No. 4, April 1991, pp. 31-44.

Wasserman, Philip D., “Neural Computing-Theory & Practice”, 1989, pp.128-129.

Weber et al., “Marquee: A Tool for Real-Time Video Logging”, CHI '94.April 1994.

Weber, Thomas E., “Software Lets Marketers Target Web Ads,” The WallStreet Journal, Apr. 21, 1997

Weiman, Liza and Moran, Tom, “A Step toward the Future”, Macworld,August 1992, pp. 129-131.

Weshsler, H. Ed., “Neural Nets For Human and Machine Perception”,Academic Press, New York (1991).

Whitefield, A. “Human Factors Aspects of Pointing as an Input Techniquein Interactive Computer Systems”, Applied Ergonomics, June 1986, 97-104.

Wiedenbeck, Susan, Lambert, Robin, and Scholtz, Jean, “Using ProtocolAnalysis to Study the User Interface”, Bulletin of the American Societyfor Information Science, June/July 1989, 25-26.

Wilf, Itzhak, “Computer, Retrieve For Me the Video Clip of the WinningGoal”, Advanced Imaging, August 1998, pp. 53-55.

Wilke, William, “Easy Operation of Instruments by Both Man and Machine”.Electro/82 Proceedings, 3/2/1-3/2/4.

Willett, P., “Recent Trends in Hierarchic Document Clustering: ACritical Review”, Information Processing & Management, vol. 24, No. 5,pp. 557-597, 1988

Willshaw et al., “Non-Holographic Associative Memory”, Nature,222:960-962 (Jun. 7, 1969).

Woolsey, K., “Multimedia Scouting”, IEEE Computer Graphics AndApplications, July 1991 pp. 26-38.

Yager, R. R., “Entropy and specificity in a mathematical theory ofEvidence”, Int. J. General Systems, 9:249-260 (1983).

Yamada et. al., “Character recognition system using a neural network”,Electronics Information Communications Association Bulletin PRU 88-58,pp. 79-86.

Yamamoto, A., et al, “Extraction of Object Features from Image and itsApplication to Image Retrieval”, IEEE 9th International Conference OnPattern Recognition, vol. 2, 1988, 988-991.

Yamamoto, A., et al, “Image Retrieval System Based on Object Features”,IEEE Publication No. CH2518-9/87/0000-0132, 1987, pp. 132-134.

Yamamoto, A., et al., “Extraction of Object Features and Its Applicationto Image Retrieval”, Trans. of IEICE, vol. E72, No. 6, 771-781 (June1989).

Yamane et al., “An Image Data Compression Method Using Two-DimensionalExtrapolative Prediction-Discrete Sine Transform”, Oct. 29-31, 1986, pp.311-316.

Yan et al., “Index Structures for Information Filtering Under the VectorSpace Model”, PROC the 10th International Conference on DataEngineering, pp. 14-18 of DRD203RW User's Manual relating to the DSSDigital System.

Yan, T. W. and Garcia-Molina, H., “SIFT—A Tool for Wide-Area InformationDissemination,” Paper presented at the USENIX Technical Conference, NewOrleans, La. (January 1995), pp. 177-186.

Yoder, Stephen Kreider, “U.S. Inventors Thrive at Electronics Show”, TheWall Street Journal, Jan. 10, 1990, B1.

Yoshida, J., “The Video-on-demand Demand”, Electronic Engineering Times,Mar. 15, 1993, pp. 1, 72.

Yoshida, Y., et al, “Description of Weather Maps and Its Application toImplementation of Weather Map Database”, IEEE 7th InternationalConference On Pattern Recognition, 1984, pp. 730-733.

Zadeh, L. A., “Fuzzy sets as a basis for a theory of possibility”, Fuzzysets and Systems 1:3-28 (1978).

Zadeh, L. A., “Fuzzy sets”, Information and Control, 8:338-353 (1965).

Zadeh, L. A., “Probability measures of fuzzy events”, Journal ofMathematical Analysis and Applications, 23:421-427 (1968).

Zeisel, Gunter, Tomas, Philippe, Tomaszewski, Peter, “An InteractiveMenu-Driven Remote Control Unit for TV-Receivers and VC-Recorders”, IEEETransactions on Consumer Electronics, 34(3):814-818.

Zenith Starsight Telecast brochure, (1994).

Zhang et al., “Developing Power Tools for Video Indexing and Retrieval”,Proceedings of SPIE Conference on Storage and Retrieval for Image andVideo Databases, San Jose, Calif., 1994.

Zhang, X., et al, “Design of a Relational Image Database ManagementSystem: IMDAT”, IEEE Publication No. TH0166-9/87/0000-0310, 1987, pp.310-314.

Zhi-Yan Xie; Brady, M., “Fractal dimension image for texturesegmentation”, ICARCV '92. Second International Conference onAutomation, Robotics and Computer Vision, p. CV-4.3/1-5 vol. 1, (1992).

Zhu, X., et al., “Feature Detector and Application to HandwrittenCharacter Recognition”, International Joint Conference on NeuralNetworks, Washington, D.C., January 1990, pp. 11-457 to II-460.

Zhuang, Yueting, Rui, Yong, Huang, Thomas S., Mehotra, Sharad, “ApplyingSemantic Association to Support Content-Based Video Retrieval”.

1-132. (canceled)
 133. A system, comprising: (a) a network interface;(b) an additional data communications interface; and (b) processor forsupporting a control interface communicated through the networkinterface according to an intermachine markup language protocol, forcontrolling the network interface and the additional data communicationsinterface.
 134. The system according to claim 133, wherein saidprocessor supports cryptographic security.
 135. The system according toclaim 133, wherein said additional data communications interfacecomprises a media data interface, wherein said processorcryptographically enforces a set of externally supplied restrictionsassociated with a media data program.
 136. The system according to claim133, wherein the network interface communicates using an Internetprotocol.
 137. The system according to claim 133, wherein the markuplanguage is XML.
 138. The system according to claim 133, wherein theprocessor supports a control interface communicated through the networkinterface according to a man-machine markup language protocol, forcontrolling the network interface and the additional data communicationsinterface.
 139. The system according to claim 138, wherein theman-machine markup language protocol comprises HTML.
 140. The systemaccording to claim 133, further comprising a mass storage interface.141. The system according to claim 140, further comprising a massstorage device for storing encrypted media, said processorcryptographically processes the encrypted media.
 142. A method,comprising: (a) providing a network interface and an additional datacommunications interface; and (b) supporting a control interface for aprocessor communicated through the network interface according to anintermachine markup language protocol, for controlling the networkinterface and the additional data communications interface.
 143. Themethod according to claim 142, wherein said processor supportscryptographic security.
 144. The method according to claim 142, whereinthe additional data communications interface comprises a media datainterface, wherein the processor cryptographically enforces a set ofexternally supplied restrictions associated with a media data program.145. The method according to claim 142, wherein the network interfacecommunicates using an Internet protocol.
 146. The method according toclaim 142, wherein the markup language is XML.
 147. The method accordingto claim 142, further comprising the steps of communicating a controlinterface through the network interface according to a man-machinemarkup language protocol, for controlling the network interface and theadditional data communications interface.
 148. The method according toclaim 147, wherein the man-machine markup language protocol comprisesHTML.
 149. The method according to claim 142, further comprisingproviding a mass storage interface.
 150. The method according to claim149, further comprising storing media, and at least one of encryptingand decrypting the media.